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Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill. • Pulp and paper mills with electric boilers can supply primary frequency reserves. • Electric boilers can provide up to 50 MW flexibility of 100 MW rating. • An algorithm is developed to optimize the energy & reserve bids of the mill. • Expected cost savings range between 7.4% in summer and 2.3% in winter. [ABSTRACT FROM AUTHOR]

The collection of sawmill residues is an important logistic activity for the pulp and paper industry, which uses the biomass as a source of energy. We study a vehicle routing problem for a network composed of a single depot and several sawmills. The sawmills serve as potential suppliers of biomass residues to the depot, which in turn processes and distributes the residues to the pulp and paper mills. This problem consists of identifying the best daily routing schedule for a fixed number of trucks. The objective is to maximize the ratio of energy returned on energy invested, while satisfying a minimum daily amount of dried biomass residues. There are several random components in the problem, including the availability and moisture content of the biomass residues. We use a combination of scenario analysis and heuristics to solve this stochastic vehicle routing problem. A performance comparison of the proposed method reveals an estimated daily energy savings of 6 GJ over the benchmark method. [ABSTRACT FROM AUTHOR]

The accelerating urbanization, rapid industrial development, and excessive consumption of fossil fuels pose survival challenges such as energy depletion and environmental degradation for humanity. In the context of industrial park development, constructing a low-carbon energy system, increasing the proportion of renewable energy, enhancing energy-level matching, and utilizing heat pumps for deep waste heat recovery are effective approaches to reduce fossil energy consumption and alleviate environmental pressures. Addressing issues such as diverse thermal flows in industrial sites, complex electricity-heat-cooling energy demands, and unclear industrial-building energy coupling mechanisms, this paper proposes a two-stage planning framework, comprising the following five steps: (1) constructing a waste heat utilization system centered around heat pumps, (2) establishing a mechanism for matching heat sources, equipment, and thermal sinks, (3) establishing an energy conversion model and energy quality quantification system, (4) optimizing waste heat utilization path allocation, and (5) conducting a system energy flow analysis. Case studies demonstrate that the proposed system achieves optimized matching of multiple heat sources and sinks in industrial and building scenarios through thermal integration, meeting diverse energy demands in the park, including electricity, cooling, and multi-grade heat. After coupling with traditional steam pipeline networks, the annual total cost and annual steam power consumption decrease by 33% and 39% respectively, with a waste heat contribution rate of up to 26% and a renewable energy penetration rate of up to 25%, and a waste heat recovery system payback period of 6 years. By establishing an energy quality quantification system and conducting multi-objective optimization considering losses and economic costs, this paper provides Pareto frontier solutions, offering references for engineering proposals. The proposed networked waste heat recovery system is characterized by low energy consumption and high economic efficiency, effectively integrating the energy characteristics of industrial parks and demonstrating engineering applicability. [Display omitted] • Focusing on industrial-building energy conjugation issues • Proposing a low-carbon energy system planning framework for industrial parks • Establishing a networked matching mechanism for cold and hot flow streams • Short system recovery cycle, good economic performance • High contribution rates of waste heat and renewable energy, evident low-carbon effect [ABSTRACT FROM AUTHOR]

As the renewable energy consumption responsibility weight system is gradually implemented in various provinces, there is a closer intersection between the excess consumption and green certificates trading. Existing trading platforms can only meet the needs of green certificates trading. This single-platform solution is no longer sufficient to meet the trading requirements under this system. Consequently, this paper proposes a excess consumption and green certificates trading platform based on blockchain technology. This platform utilizes the decentralized, open, and anonymous characteristics of blockchain technology and rely on its smart contract capabilities to simultaneously meet the comprehensive trading needs of surplus consumption and green certificates trading through combining user-initiated transactions with platform-intelligent recommendations. It adapts to the complex excess consumption and green certificates trading market that has emerged following the issuance of the renewable energy consumption responsibility weighting policy. To conduct simulated transactions, 16 groups of buyers simulate a total of 215,792 excess consumption vouchers and 143,861 green certificates in this paper. The transaction results all meet the expected transaction scenarios under the conditions of smart recommendation, demonstrating the platform's transaction feasibility. [Display omitted] • Design a blockchain-based smart contract solution for volume consumption trading and green certificate trading. • Build a database containing 215,792 excess consumption vouchers and 143,861 green power certificates. • Simulate the purchase of 160 buyers by establish a transaction platform. • Analyzed the application direction of the data produced by the trading platform [ABSTRACT FROM AUTHOR]

As the proportion of behind-the-meter (BTM) renewable energy resources (RERs) in the distribution network continues to rise, the precise role of RERs in the flexibility market, whether as "Recipients" or "Providers", is becoming less distinct. This observation has led us to explore the integration of flexible load resources and RERs into flexibility markets. Such integration facilitates resource sharing among multiple microgrids (MGs). This paper focuses on the potential emergence of a secondary flexibility (SecFlex) market in the power sector among high proportional BTM-RERs microgrids. Within the concept of SecFlex market, this paper outlines a comprehensive process supporting secondary flexibility trading, which includes "SecFlex market organization–trading volume collation–response schedule optimization". At first, the market mechanism of the SecFlex market is proposed, where players submit an empirical load curve to the market organizer. During the clearing stage, a social welfare equilibrium problem is solved. Subsequently, the equilibrium trading volume is adjusted in the collation stage to account for the distribution network (DN) voltage magnitude balance. Finally, a response schedule strategy is designed to analyze the behaviors of the players. The cvxpy framework is used to solve social welfare and competitive equilibrium in this paper. The results show significant cost reductions for two players, by 77.8% and 24.0% respectively. However, costs for Player 3 have increased substantially, up to 4.6 times the baseline when flexibility is limited. These findings provide theoretical guidance for the development of the SecFlex market. • A secondary flexibility market mechanism with high share of BTM RERS. • A comprehensive process including market, technical, and control aspects. • A trading volume collation method that supports the secondary flexibility market. • Discussion and control analysis of different behavior patterns of market players. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases. • A new hybrid wind-wave-PV system is established for the first time; • An exponential slime mould algorithm is proposed; • The comparison of the six algorithms highlights the ability of the exponential slime mould algorithm to optimize the array layout of new hybrid wind-wave -PV system. [ABSTRACT FROM AUTHOR]

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper. • This paper proposes a method to characterize the dispatching boundary of VPP based on the FOR. • Constructs the static voltage security region (SVSR) related to each node. • Thermal security region (THSR) related to each branch in the cluster in the power injection space. • This paper proposes a strategy to identify and eliminate the ineffective constraint • The boundary construction method of VPP is applied to the cooperative optimal dispatching. [ABSTRACT FROM AUTHOR]

The rapid development of cyber-physical power system has highlighted the pressing issue of cyber attacks (CAs) faced by large-scale renewable energy power systems (LREPS). The injection of false data attacks on critical electrical equipment by attackers can lead to abnormal system frequencies, triggering cascade failures and causing widespread blackouts. This paper introduces a tri-level defense model specifically crafted for LREPS, comprising an upper, middle, and lower level (defender-attacker-operator). By promoting collaborative decision-making among these agents, the model aims to mitigate the rate of change of frequency (RoCoF) and effectively withstand CAs. The upper-level employs an economic-inertia optimization strategy, scheduling generator units to maximize system inertia while minimizing generation costs. By leveraging the strong duality theorem, the tri-level model is decomposed into a bi-level model consisting of a master problem and a subproblem, solved using the Benders decomposition method. Furthermore, this paper also considers augmenting the virtual inertia of renewable energy units when defense resources are insufficient, ensuring RoCoF remains within limits after CAs. The effectiveness and superiority of the proposed model and strategy are validated through case studies based on the improved IEEE 118-bus system. The results demonstrate that the model can reduce load shedding caused by the worst-case CAs, enhance system inertia, lower RoCoF , and improve the stability of LREPS. • Propose a defense model against frequency disturbances caused by cyber attacks. • Minimize load shedding under worst-case attack. • Enhance system inertia through economic-inertia optimization. • Model significantly reduces rate of change of frequency. [ABSTRACT FROM AUTHOR]

The dispatchable region (DR) describes the ability of a power system to adapt to the variability of distributed renewable energy (DRE) generation. Switchable devices such as soft open points (SOPs) and tie switches enable the AC–DC distribution network to flexibly cope with DRE power fluctuations. Currently, research on DRs seldom considers the effects of SOPs and reconfiguration with tie switches in AC–DC distribution networks, which prevents accurate evaluation of DR for this type of network. To bridge this gap, this paper formulates DRs for DRE generation in an AC–DC distribution network considering SOPs and reconfiguration with tie switches. The original model for deriving DRs is typically nonconvex, and the DR boundaries are intractable because of nonlinear power flow equations and binary variables of tie switches. Accordingly, this paper employs the outer polyhedral approximation and convex hull relaxation to approximate the original DR. An efficient adaptive constraint generation algorithm is developed to search for boundaries of the approximated DR. The numerical results show that the proposed model can effectively adapt to the DR of reconfigurable AC–DC distribution networks with SOPs and verify the accuracy and computational efficiency of the proposed method. • DRs of DRE generation are formulated in AC–DC distribution networks considering SOPs and tie switches. • Power flow equations and reconfiguration constraints in the AC-DC network are handled using polyhedral approximation and convex hull relaxation. • The accuracy and computational performance of the proposed method is verified. [ABSTRACT FROM AUTHOR]

Under the background of green certificate trading (GCT)‑carbon emission trading (CET) market, the application of P2P trading in regional integrated energy system (RIES) has not been fully explored, and it is urgent to design a reasonable prosumer P2P trading mechanism to adapt to the development of diversified electricity market. Therefore, this paper proposes a prosumer P2P cooperative trading model based on generalized Nash bargaining. Specifically, based on the differential modeling of various types of prosumers, this paper constructs a prosumer P2P cooperative trading model based on generalized Nash bargaining theory, and quantifies the bargaining power of prosumers through the contribution value of interactive operation. The GCT-CET collaborative mechanism is introduced to promote the consumption of distributed energy by transforming carbon quotas through green certificates. In addition, this paper designs a warm start and adaptive step alternating direction multiplier method decentralized algorithm (WAS-ADMM) to improve the solution efficiency and protect the trading privacy of prosumers. Through a case study, it analyzes the impact of prosumers' P2P cooperative trading on the consumption of distributed energy, prosumers' GCT-CET costs and comprehensive costs in the context of GCT and CET market. The simulation results show that the proposed trading model reduces the GCT-CET costs of the prosumers' alliance by about 24.98%. • A P2P cooperative trading model of integrated energy is constructed based on GNB. • A GCT-CET collaborative mechanism is proposed to promote the consumption of renewable energy. • An improved decentralized algorithm based on WAS-ADMM is designed to solve the P2P trading model. [ABSTRACT FROM AUTHOR]

In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low-carbon transformation of the power sector. [Display omitted] • Design a cooperation mode of new energy power stations and shared energy storage. • Divid the shared energy storage into physical energy storage and virtual energy storage. • Propose a two-stage robust optimization model with improved uncertainty interval. • Construct an entropy weight modified Shapley value-based benefit allocation strategy. • Select a new energy base as the simulation system for verifying the effectiveness and applicability. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

With deepening decarbonization and increased Renewable Energy Sources (RESs) integration, the power system's inertia has declined, affecting the network's ability to balance power at the distribution level. Concurrently, the proliferation of prosumers presents a regulatory opportunity for Distribution System Operators (DSOs), despite the complexity introduced by their high number and varied behaviors. This paper introduces a new co-scheduling model optimizing prosumers' capacities through Virtual Power Plants (VPPs) in local networks, enhancing DSO oversight and facilitating prosumer participation in regulation markets. The proposed model concurrently schedules energy provision alongside voltage and frequency regulation capacities. Recognizing prosumers' behavioral uncertainties, Data-Driven Distributionally Robust Optimization (DDRO) is employed to ensure adequate capacity for VPP engagement. Importantly, the paper outlines a mechanism allowing DSOs to partner with multiple privately-owned VPPs, ensuring privacy through an adaptive Alternative Direction Method of Multipliers (ADMM) method. This method avoids the exchange of sensitive information, ensuring confidentiality and scalability. Consequently, VPPs can proficiently manage scheduling and communicate their regulation capacities. The operator then dispatches control signals based on regulation needs and network flow. Results from the IEEE 33 bus test system confirm the model's efficacy in enhancing voltage support and frequency regulation, and generating revenue for both VPPs and prosumers. • This paper proposes a new co-optimization strategy for distribution-level VPPs. • A distributed coordination approach between DSO and VPPs is presented. • An adaptive consensus ADMM is developed to model the communications. • The uncertain nature of prosumer behavior is addressed by a DDRO model. • The effectiveness of the developed approaches is extensively illustrated. [ABSTRACT FROM AUTHOR]

Aggregating a waste gasification unit with renewable energy sources (RES) into a virtual power plant (VPP) not only effectively reduces the negative impacts of renewable energy sources and improves the performance of VPP in the electricity market, but also participates in the hydrogen market by purifying the hydrogen in the syngas produced after gasification. A market bidding strategy that improves the profitability of the system is the basis for sustainable VPP. Based on this, this paper for the first time aggregates a waste gasification unit with RES into a VPP named WtE-VPP. Then, considering that the WtE-VPP system can generate both electricity and hydrogen and their outputs affect each other, this paper constructs a bi-level bidding strategy model for the combined electricity‑hydrogen market. The lower-level model aims to minimize the total social costs of the electricity and hydrogen markets, while the upper-level model aims to maximize the profits of WtE-VPP. Finally, the model is tested on an improved PJM 5-node grid with a 7-node hydrogen network, and the results show that the method is effective in reflecting strategic behaviors of WtE-VPP. Compared to participation in a single electricity market, the total benefits of WtE-VPP participation in both electricity and hydrogen markets increase by 39.18%. • Design a novel VPP aggregated waste gasification and renewable energy sources. • The coupling of electricity and hydrogen output is studied in the novel VPP. • Development of a joint bidding strategy for electricity and hydrogen markets. • A bi-level model is used to balance the social costs and the benefits of the VPP. [ABSTRACT FROM AUTHOR]

An effective way to promote the consumption of renewable energy resource generation is by forming a joint power supply system between photovoltaic (PV) power plants widely distributed along a river basin and large-scale cascade hydropower plants, using shared transmission lines to transmit power to the power grid system. However, renewable energy resource generation has significant prediction uncertainty and can flood into the power grid, which is detrimental to its safe and stable operation. To solve the above issues, this paper proposes an improved interval optimization method to model the uncertainties of the inflow runoff of a cascade hydropower plant and the photovoltaic power output. On this basis, a medium- and long-term interval optimization scheduling model is established for a cascade hydro-PV complementary system based on extreme scenarios. This model does not need to obtain an accurate probability density distribution for renewable energy, and can realize the overall optimization of runoff-PV uncertainty scenarios within a certain range, taking into account the economy and robustness of system operation. In order to ensure the solution efficiency, the original model is transformed into a mixed integer linear programming (MILP) model by a variety of deterministic transformation methods and linearization techniques. The case study shows that: 1) Under the premise that the system power output is relatively stable, the power curtailment of the interval optimized scheduling scheme proposed in this paper is reduced by 47.5% compared with the robust scheduling scheme. The ability to cope with extreme scenarios is also significantly improved compared with the deterministic scheduling scheme. 2) There is a game relationship between the robustness and economy of the obtained scheduling scheme. When the fluctuation interval width of random variables increases from 20% to 30% and 40%, the corresponding total power generation decreases by 2.4% and 4.7%, respectively. 3) A large penalty factor is conducive to reducing the amount of abandoned hydropower, but in general, the penalty factor and ecological flow level have no obvious influence on the dispatching results, and the values of both can be reasonably determined according to the actual operation requirements. • An improved interval optimization method is proposed to deal with the uncertainty of inflow runoff and PV power output. • A MILP model for the medium- and long-term operation of a cascade hydro-PV complementary system based on extreme scenarios is established. • The resulting scheduling scheme achieves balance between economy and robustness. • The influence of penalty factor and ecological flow on the dispatching results is not obvious. [ABSTRACT FROM AUTHOR]

To accommodate the stochasticity of variable renewable energy sources (VRES) while efficiently dispatching generation resources and procuring adequate reserves, previous research proposed co-optimizing energy and reserves in the day-ahead (DA) using various uncertainty-based mechanisms. However, the co-optimized markets based on these mechanisms exhibit implementation limitations related to their high computational burden, complex customized solution algorithms, and over-conservative solutions. To address these shortcomings, this paper proposes a practical light robust optimization (LR) approach for the DA co-optimization of energy and reserves. The method results in a linear market clearing mechanism that easily enables the control of the robustness level of the solution through a tunable conservativeness parameter. In addition, the paper explores three different formulations for specifying the system reserve requirements considering, namely, fixed reserve requirements (LRF1), variable reserve requirements based on system uncertainty (LRF2), and a combined approach (LRF3). The formulations integrate the uncertainty from VRES in the market setting using a new bid format called uncertainty bid. The three formulations are then compared using a case study. The numerical results show the effects of the variation of the conservativeness parameter and the reserve requirements on the total socio-economic welfare (SEW), dispatched energy quantities, anticipated activation costs, and procured reserves. Moreover, the analyses showcase that sizing reserves based on system uncertainty (in LRF2) results in a 27%–61% decrease in reserve procurement costs when compared with LRF1, while the combined approach (in LRF3) results in a better performance than LRF2 in terms of reserve activation costs, with costs 61%–263% lower than in LRF2. • A light robust approach for the co-optimization of energy and reserves is proposed. • The proposed approach yields an easy-to-solve linear market-clearing mechanism. • The market operator chooses the level of robustness of the co-optimized solution. • Uncertainty bids considering VRES positive and negative deviations are accepted. • Different reserve requirements sizing methods are proposed and compared. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [ABSTRACT FROM AUTHOR]

Long-distance power support through High-voltage Direct Current (HVDC) has provided feasible solutions for power dispatch and control problems in multi-area power systems under high share of renewable energy. In this paper, an advanced multi-area intra-day dispatch strategy for power systems with high penetration of renewable energy considering power support capacity is established to increase the accommodation of wind power and enhance the stability of the power system. At first, a novel assessment method of regional active power support capacity is carried out in this paper, which can sufficiently quantify the regional power margin (RPM), the DC dispatchable power (DCDP), and the regional power support distance (RPSD) of a power system. Subsequently, a multi-area intra-day dispatch strategy based on power support capacity analysis is proposed, where constraints on conventional units, wind farms, transmission lines, and RPSD are developed. Finally, to enable the development of the dispatch model, a model predictive control (MPC) scheme is used to update the power support capacity indicators dynamically and improve model reliability. The effectiveness and superiority of the proposed strategy are verified by case studies. Numerical results show a 7.02% cost reduction and overload risk reduction by using the proposed strategy compared to conventional global optimization methods. • A novel assessment method of regional active power support capacity is proposed. • A multi-area dispatch strategy considering power support capacity is established. • A model predictive control scheme updates the power support capacity indicators. • Proposed strategy enhances the power system stability and reduces dispatch costs. [ABSTRACT FROM AUTHOR]

The trend towards reducing greenhouse gas emissions by dipping the use of traditional sources of energy in marine power grids, as well as the rapid expansion of renewable energy sources (RESs), were the driving forces behind the incorporation of RESs in maritime microgrid systems and enquiry of the ensuing prevalent control mechanism. The frequency stability of a Marine Microgrid System (MMGS) is a critical aspect that directly affects its reliable and efficient operation. This study describes a method for frequency stabilization in independent maritime microgrids consists of diverse renewable energy resources including wind turbine generators, sea wave energy/tidal power generation, solar generation, bio-diesel generator and energy storage systems. This paper aims to develop a new optimal cascaded order proportional integral based proportional derivative for marine load frequency control. Due to the fact that the performance of the controller is highly dependent on the parameters of the controller, optimizing these coefficients can have a significant impact on the output performance of the LFC control. In light of this, this paper presents the sewing training-based optimization (STBO), a new human-based metaheuristic algorithm to optimize the coefficient of the suggested controller. The essential stimulation of STBO is the teaching procedure of sewing to beginner tailors. By utilizing a population of candidate solutions, the algorithm iteratively refines the control parameters to find the optimal set that minimizes frequency deviations and enhances system stability. The responses of the cascaded PI-PD controller are linked to those of the PID and PI controllers in order to demonstrate its superiority. To evaluate the efficiency of STBO, it is compared to well-established recent techniques such as fitness dependent optimization, grey wolf optimization and Jellyfish search optimization. From the results, it is observed that our proposed approach improved the settling time by 25.35%, 45.89%, and 29.35%, reduced peak overshoot by 78.34%, 67.71%, and 78.23%, and similarly reduced undershoot by 81.56%, 56.22% and 76.56% as compared to FDO, GWO and JSO techniques respectively. Finally, the sensitivity analysis is performed under ±50% load variation and ± 40% power system parameters to prove the robustness of the proposed controller. • A diverse renewable energy resources including renewable energy resources and energy storage systems (ESS,s) incorporating with battery energy storage, capacitive energy storage, and flywheel energy storage system has been considered for marine microgrid. • A novel cascaded order PI-PD controller has been designed and implemented for the suggested distinct renewable and energy storage based maritime microgrid. • The coefficient of the proposed controller is optimized with a new human-based meta-heuristic algorithm known as sewing training-based optimization (STBO). • Multi objective functions including integral time absolute error (ITAE), integral time square error (ITSE) and integral square error (ISE) are formulated for the proposed power system. • Finally, sensitivity testing of the STBO adjusted PI-PD controller under uncertain parameters such as turbine gain variation (K CE), droop factor (R), and inertia constant (M) is performed to prove the robustness of the proposed frequency control technique. [ABSTRACT FROM AUTHOR]

Shared energy storage is widely recognized as an energy hub for the coordinated operation of regional integrated energy systems (RIESs). Multi-energy systems (MESs) share centralized energy storage to store excess renewable energy sources (RESs). This paper proposes a novel energy-sharing framework considering hydrogen trading, which is suitable for residential, industrial, and commercial RIES structures. Shared energy storage operator (SESO) promotes hydrogen energy transactions by formulating time-of-use (TOU) hydrogen prices. The proposed hydrogen energy trading method can be regarded as a master-client structure, and a hierarchical optimal scheduling method based on the Stackelberg game relationship is designed. Finally, a case study is performed based on an optimal scheduling algorithm combining particle swarm optimization (PSO) and mixed integer linear programming (MILP). The cases show the electricity‑hydrogen shared energy storage mechanism in RIESs can improve the RESs utilization rate and effectively reduce the operating costs of each system. Moreover, compared with RIESs with a single centralized electric energy storage, the TOU hydrogen price mechanism can further lower the energy prices and improve the economy of the RIES. The cases show that hydrogen energy sharing reduces the operating cost of RIES by 9.96% and increases the regional energy utilization rate by 2.97%. And the shared energy storage business model can satisfy the economy of both SESO and RIES. It confirms the rationality of the electric hydrogen shared energy storage project. • A sharing energy storage mechanism in multi-energy systems is presented in this paper. • The hydrogen trading methods based on time-of-use hydrogen prices are considered in this paper. • A hierarchical optimal scheduling method based on the Stackelberg game is designed. [ABSTRACT FROM AUTHOR]

The district heating system (DHS) is one of the essential carriers for coordinating renewable energy with fossil energy and achieving flexible energy consumption. Considering the uncertainty of both the renewable energy units' power and the consumers' aggregate loads impact the transport process of the DHS, the thermal characteristics of the DHS transportation and the node temperature response under the source-load uncertainty need to be quantified and analyzed. This paper proposed a method to calculate the nodes' temperature dynamic response of the DHS under multiple source-load uncertainty scenarios. The prediction and combination methods of supply and demand probability intervals of source and load nodes under varying credibility levels were investigated. Additionally, the effects of source-load uncertainty boundary conditions on the DHS transport process were analyzed. The temperature response of the nodes was calculated based on the established dynamic DHS model under multiple source-load uncertainty scenarios. A DHS in Beijing was selected for case validation and analysis, and it has 90 nodes and 109 pipes. The simulation results showed that the relative average error of node flow rate in the dynamic DHS model was 3.02%, and the average difference in node temperature was 1 °C. The probability distribution semi-analytical method and the Bayesian credible interval calculation method can accurately describe the uncertainty on both source and load sides. The models and algorithms proposed in this paper could effectively quantify the nodes' dynamic temperature response under the source-load uncertainty. • Proposed uncertainty quantification models for the source and load nodes of DHS. • Developed a novel credible interval calculation method for nodes' thermal power. • Quantified nodes' temperature dynamic response considering source-load uncertainty. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES. • Novel Index for PV-P2G integration (PRPI). • Enhancing Acceptance rate of PV applying PRPI • Reduced optimization complexity. • Adaptable to various renewables. [ABSTRACT FROM AUTHOR]

• The optimization problem of EPS and flexibility-enhanced DHSis formulated. • The DHS is retrofitted with BCT and uses a variable mass flow operation strategy. • A conditional-DRO is employed to hedge against the uncertainty of renewable energy. • Propose adouble-layer distributed algorithm to achieve the distributed solution. In an integrated electricity and heating system (IEHS), building flexibility-enhanced district heating systems (DHSs) that use advanced flexibility-enhanced technologies are effective for promoting renewable energy accommodation. However, it is challenging to comprehensively utilize these flexibility-enhanced technologies in IEHS dispatch. This paper proposes a distributed conditional-distributionally robust coordination method for an electrical power and flexibility-enhanced DHS. The DHS is retrofitted with bypass compensation technology and uses a variable mass flow operation strategy to enhance its flexibility and promote renewable energy accommodation. Moreover, conditional-distributionally robust optimization is used to consider the inherent dependency between the forecasting error of a wind farm and the forecast output information, which goes beyond the state-of-the-art models. Additionally, a novel reformulation method is adopted to transform the problem with random variables and bilinear constraints into a more tractable programming problem, yielding a conservative approximative model of the original model that guarantees feasibility. Finally, to protect the privacy of electrical power system and DHS operators, this paper proposes a double-layer distributed algorithm to solve the problem in a distributed fashion while guaranteeing feasibility. Case studies demonstrate that the proposed model with flexibility-enhanced DHSs significantly promotes renewable energy accommodation and reduces dispatch costs, and the proposed double-layer distributed algorithm effectively solves the dispatch problem in a distributed fashion and guarantees the feasibility of dispatch results. [ABSTRACT FROM AUTHOR]

Carbon neutrality is one of the main goals in current power system planning and operation. Many different actions and solutions are presented and implemented so far, incorporating renewable energy sources at all levels of the system. With the raising prices of natural gas and its negative impact on the environment, particularly emphasised over the past two years, new technologies and energy vectors are emerging as potential for its replacement. High importance of the hydrogen energy vector, especially in the decarbonisation of the industry sector, is being put forward due to its advantages of zero greenhouse gas emissions, its capacity for storing energy and capability to balance the production of renewable energy sources. This paper brings a detailed mathematical model of a price driven, demand responsive, multi-energy industry facility, as a logic first implementer of hydrogen technologies due to its high and multi-energy consumption nature. The systematic analyses are conducted over a set of scenarios of local production, considering different hydrogen technologies as well as range of natural gas and electricity prices. The findings of the paper conclude that hydrogen technology implementation into a realistic industrial consumers processes results in zero local emissions production, high level of autonomy and resistance from the market disturbances. When compared with classic industrial layouts, the overall CO 2 footprint is reduced from around 30% to around 85%, depending on the scenario. The sensitivity analysis has proven that hydrogen layouts are comparable to natural gas layouts in terms of total costs, showing that hydrogen options result in lower cost in the range from 25% to 58%, depending on the observed scenario. • Integration of renewables and hydrogen technologies in industrial plant. • Price sensitivity analysis showcasing economic benefits of different hydrogen technologies. • Carbon footprint reduction up to 80% from the business-as-usual approach. • Complete reduction in local GHG emissions using hydrogen technologies. [ABSTRACT FROM AUTHOR]

To decarbonize the heating sector, the 4th and 5th generations of district heating networks have been identified as promising solutions. They offer superior energy efficiency, economic viability, and environmental advantages compared to decentralized, individual heating systems. However, they raise several challenges concerning their design and control optimization due to their size and the operational constraints of the production systems involved such as inertial heat generators, intermittent renewable energy sources and thermal energy storage. As a result, numerical simulations of these networks are computationally heavy which makes fast optimal control a complex and challenging task. A common strategy to address this limitation is to formulate reduced order models or to establish fast and yet accurate surrogate models. In this work, we present a novel surrogate modeling framework to rapidly predict district heating networks transients. Our model is based on a physics-guided spatio-temporal convolutional graph neural network. While similar work focused mainly on the prediction of thermal loads, this paper tackles the challenge of simulating complex and non-linear behaviors of the distribution network of district heating systems. The results show that the simulation time using our model is 99% less than a physical simulator while maintaining a high accuracy. In addition, we have conducted an ablation study and a residual analysis to test the robustness of the proposed model. Furthermore, the generalization ability of our approach is assessed by evaluating it against different district heating network topologies. • A spatio-temporal graph neural network is used to predict district heating network dynamics. • A framework enables interaction between physical simulator and surrogate model. • The model achieves high accuracy and captures diverse network dynamics. • Computation time reduction unlocks global optimization with full dynamics of the system. [ABSTRACT FROM AUTHOR]

The development of 100% renewable energy (RE) systems provides a viable solution for achieving the global target of carbon neutrality. To support the reliable and economical operation of RE-based local energy networks, this paper presents a joint scheduling model for grid-scale RE generation and hydrogen-based flexible loads. The direct load control (DLC) through hydrogen-electrical microgrids is analytically modeled for leveraging the intrinsic flexibility of demand-side multi-energy synergy. To handle the uncertainty and volatility of RE generation, a risk-averse stochastic programming method with the receding-horizon mechanism is developed. Also, the power balancing cost in scheduling objectives is represented as a conditional value-at-risk (CVaR) measure to control the risks of fully RE supply. Case studies on an exemplary RE system confirm the effectiveness and economic benefits of the proposed method. The hydrogen-enabled DLC can largely mitigate the supply–demand mismatches, which shows a great potential to facilitate 100% RE scenarios. • A joint scheduling model of grid-scale renewable energy generation and hydrogen-based flexible loads is proposed to achieve carbon neutrality. • Direct load control through hydrogen-electrical microgrids is analytically modeled and implemented. • A risk-averse stochastic receding-horizon scheduling method is developed to handle the uncertainty of renewable energy generation. • Power balancing cost is represented as conditional value-at-risk (CVaR) to control the operational risks. • Results of case studies indicate the feasibility and cost-effectiveness of methods application for a 100% renewable energy scenario. [ABSTRACT FROM AUTHOR]

The situation in an urban distribution network becomes intricate under high-impact, low-probability events. Furthermore, the growing quantity of components within the urban distribution network renders their analysis more challenging. Thus, the desired recovery strategy should not only meet the requirements of restoration rates but also guarantee a stable power supply. To address this issue, this paper proposes a multi-objective decision analysis framework, which consists of EV user requirements, MPS routing, and emergency repair crew schedule, to generate a more precise recovery strategy. These objectives' dynamic behaviors in the transportation network are transformed into spatiotemporal characteristic variables and introduced into inequality constraints. Also, the Wasserstein generative adversarial network with gradient penalty is employed in this framework to characterize solar and wind realistic output. By minimizing the weighted sum composed of MPS operational costs, EV user expenses, load loss, and voltage fluctuation, an optimized recovery strategy is provided to the grid operator. Case studies investigate the effectiveness of recovery strategy in IEEE 33-bus and 123-bus benchmark systems. The results indicate the proposed approach can effectively handle complex scenarios achieving rapid and reliable restoration, while also preserving regular charging behavior and maintaining a stable power voltage supply. • Enhance resilience for urban distribution networks under disaster events. • Coordinate multiple recovery components and renewable energy integration. • Consider public requirements for fast restoration, power quality, and EV charging. • Involve renewable energy uncertainty generation. • Identify the strategy selection in response to energy uncertainty. [ABSTRACT FROM AUTHOR]

Grid-Tied Converters (GTCs) serve a vital role in integrating renewable energy generation into power networks with high penetration rates. During a grid voltage sag or recovery, the existing LVRT technologies still suffer from overcurrent caused by fault detection delay under the control of the Virtual Synchronous Generator (VSG). This vulnerability may result in trip-off occurrences or damage to the power electronic components. At present, VSG-related research mainly focuses on control and stability analysis, etc. There is a relative lack of work on the fault current-voltage (I-V) characteristics of VSG during grid voltage dips, which is urgently needed. Due to this indistinct nonlinear characteristics, the grid-forming GTC either loses the voltage source characteristics of the VSG or deteriorates the transient stability against deep asymmetrical grid dips. As a result, the conventional solutions provide limited assurance of transient stability and inadequate management of asymmetric grid disturbances. The analysis in this paper begins with a dimensional examination of the I-V characteristics of the VSG (IV-VSG) during grid contingencies. Subsequently, this research presents an improved control strategy for increasing the LVRT capability of GTCs under different grid-fault circumstances. Additionally, a rigorous analysis of transient stability is performed to uphold the synchronization between the VSG and the grid during severe grid disturbances. Both the simulation and experimental verification demonstrate that fault currents are restricted at a rapid rate, power synchronization is maintained and the need for an enlarged GTC and current limiting strategies are eliminated. In addition, the transient stability of the VSG is validated under a variety of fault conditions. • A novel low-voltage ride-through (LVRT) control scheme is proposed to fully comply with the grid codes. • The LVRT strategy effectively restricts the fault currents while simultaneously maintaining the characteristics of the VSG. • The transient stability is rigorously analysed to ensure the synchronism under a severe grid voltage dip. • Sophisticated gain tuning, specific fault-current limiter or additional control is not required for the implementation. [ABSTRACT FROM AUTHOR]

Ultra-low frequency vibration in ambient environment is an abundant renewable energy source. Corresponding energy harvesters have attracted increasing attention from both academic and industrial worlds. In this paper, a rotational electromagnetic energy harvester (EMEH) and a translational EMEH are combined into a spring pendulum system, thus forming a dual EMEH, in which internal resonance phenomenon is triggered to result in significantly enhanced energy harvesting efficiency. The spring pendulum system exhibits robustness and stability with the equivalent electromagnetic shunt damping constraints. Meanwhile, the tunable damping preserves the integrity of the system even the external excitation overloads. Experimental results demonstrate that the output power of the dual EMEHs can reach up to 0.9 W with a moving mass of 1.42 kg. Moreover, the swing angle of the pendulum can be reduced with the effect of internal resonance, which protects the main system and ensures its operation in harsh operating environment. • Dual electromagnetic meachanisms enable watt-level output power. • Pendulum swinging amplitude is restricted with even larger ouput power. • Internal resonance for frequency up-conversion is realized. [ABSTRACT FROM AUTHOR]

Policymakers are in a balancing act when creating local energy transition strategies. Embedding new technologies in an existing energy system is highly complex. Policymakers must deal with multi-system interactions such as sector coupling, multi-scale effects such as bottom-up behavior and top-down policies, and requirements from local spatial planning, grid constraints, and resource availability. Decision support tools can help to navigate this complex landscape. This paper showcases a tool to support policymakers with heating strategies for Dutch neighborhoods. The tool is a GIS-based simulation model of the energy system developed using a collaborative approach and applied in a scenario study. Energy calculations are done over a year with an hourly resolution, while scenarios can include any future energy system configuration. The results highlight trade-offs between heating strategies, interaction effects with the mobility and electricity transitions, and bottlenecks in transition pathways. Collective district heating has less grid impact but higher emissions and costs, while individual (hybrid) heat pumps have lower emissions and costs but more grid impact. No-regrets and enabling technologies are insulation and smart charging of electric vehicles and boilers. Collaborative modeling with a GIS-based user-interface increases system understanding, including trade-offs, transition pathways, and bottlenecks, in a collective and interactive way. This creates a shared and well-grounded vision, resulting in robust local renewable energy strategies. • A multi-system, multi-scale decision support tool is an essential contribution to local renewable energy system planning. • There is no clear winner in renewable heating strategies in residential neighborhoods in The Netherlands. • Trade-offs occur in terms of costs, grid congestion, and emission reductions. • Models can be used to identify lock-ins and bottlenecks in energy transition pathways. • Collaborative modeling for renewable energy strategies increases system understanding and more robust decision-making. [ABSTRACT FROM AUTHOR]

This paper presents a novel data-driven approach that leverages reinforcement learning to enhance the efficiency and safety of existing energy flexibility controllers, addressing challenges posed by the dynamic and uncertain nature of modern energy landscapes. With the increasing integration of renewable energy sources, conventional controllers struggle to maintain both safety and optimality. Our proposed approach introduces two significant contributions to standard RL approaches: a data-driven predictive safety filter and an online changepoint detection and policy updating module. Through continuous constraint satisfaction, the predictive safety filter guarantees absolute safety of the proposed controller. Meanwhile, the changepoint detection and policy updating module, inspired by the concept of continual learning, enhances the controller's adaptivity to non-stationary environments. By identifying changes in the environment, it triggers relearning of the agent, making the controller resilient to evolving conditions. Validation of our approach is conducted on a grid-connected PV-battery-load system, demonstrating its effectiveness in simultaneously improving safety and performance over traditional learning methods. More specifically, the proposed solution was able to increase the energy flexibility by reducing energy costs with 9.3%. • Proposed an innovative control framework employing reinforcement learning to enhance energy flexibility. • Incorporated a changepoint detection and policy updating mechanism to address the challenge of dynamic environments. • Introduced a predictive safety filter to enhance the safety of current reinforcement learning methods. • Validated effectiveness in a case study, demonstrating improved energy flexibility and safety. [ABSTRACT FROM AUTHOR]

Online energy management utilizing the real-time information of a residential microgrid (RM) can make full use of renewable energy and demand-side resources at the residential level. However, existing online energy management methods for RMs have poor robustness against environmental changes, which limits their applicability in highly uncertain scenarios. To address this, a novel online energy management method based on the prioritized sum-tree experience replay strategy with a double delayed deep deterministic policy gradient (PSTER-TD3) is proposed in this paper. First, we formulate the sequential scheduling decision problem as a Markov decision process (MDP) problem with the objective of minimizing residential energy costs while simultaneously ensuring household thermal comfort and minimizing range anxiety for electric vehicle usage. Then, using the proposed method, we determine the optimal online scheduling strategy under this objective. By integrating the prioritized experience replay strategy of the summation tree structure into TD3, the agent is able to learn the optimal scheduling strategy in complex environments, and its optimization performance and policy learning efficiency are significantly improved. In addition, its ability to handle multidimensional continuous action spaces helps achieve finer-grained optimization for RMs. The case study results demonstrate that the proposed method can effectively reduce the energy costs of residential microgrids while satisfying household thermal comfort requirements and reducing range anxiety for electric vehicle usage. Moreover, the optimization performance of the proposed method is robust when the uncertainty factors fluctuate violently in the environment. • A MDP with unknown state transition probabilities is established. • the thermal comfort and range anxiety are considered in the HEMS joint scheduling. • A RM online energy management method based on a novel DRL method is proposed. • Results show the superior performance of the proposed energy management method. [ABSTRACT FROM AUTHOR]

Air-conditioners (ACs) can fully utilize the inherent characteristics of storing heat/cold in demand response (DR), achieving peak load shifting and renewable energy consumption. However, traditional control strategies based on compressor ON/OFF state require retrofitting of the internal components of ACs, making it challenging to implement directly through ACs' infrared control protocol. Given the widespread availability of infrared temperature-set-point (TSP) adjustment functions in existing ACs, this paper proposes a day-ahead optimal scheduling strategy of aggregate ACs under TSP control. Based on the second-order equivalent thermodynamic parameter (ETP) model, the TSP adjustment characteristics are considered to establish the equivalent energy storage (EES) model of ACs. On this basis, taking the overall optimization objectives of power system into account, the optimal scheduling model of aggregate ACs under TSP control is established to solve collaborative optimization problems for power generation and consumption. Case study demonstrates that, compared to optimal scheduling models based on traditional EES model, the proposed model exhibits superior peak load shifting effects and allows for smooth control of ACs through the issuance of control commands. • An optimal scheduling model of aggregate air-conditioners based on equivalent energy storage model is established. • Discrete temperature-set-point control realized through air-conditioners' infrared control protocol. • The aggregate air-conditioners' energy storage capacity and their potential in demand response are enhanced. [ABSTRACT FROM AUTHOR]

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs. • We generate climate change-informed load and renewable energy production profiles. • We extend a design-and-dispatch problem to a two-stage stochastic program. • Cases with high variation in annual renewable output show the methodology's value. [ABSTRACT FROM AUTHOR]

The transition to renewable energy sources, particularly sources like wind and solar induces a dependency on weather in the supply side of electrical grids. At the same time, the move to electric mobility with uncontrolled charging induces extra peak loads on these grids. These developments can cause grid congestion or an imbalance between the renewable power supply and the demand. Locally balancing the power supply and demand in grid-connected microgrids can alleviate such issues on the main grid. This paper presents a model based control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid to avoid reaching the threshold rated power output set for large buildings. The microgrid under consideration consists of photovoltaic power sources and a large fleet of electric vehicle chargers (> 150). A model predictive controller is developed that treats the daily vehicle charging as a batch process. Given vehicle charge objectives, the controller utilizes vehicle charger occupancy and photovoltaic power generation forecasting services to distribute power optimally over a fixed period of time. The optimization problem is formulated as a quadratic programming problem and is implemented utilizing open-source Python libraries. The controller was integrated into the control system of a microgrid situated at a corporate office in the Netherlands. The control system oversaw the operation of 174 vehicle chargers. The effectiveness of the model predictive control technology was demonstrated over a three-week period and led to an average daily grid peak power reduction of 59%. • A model-based control strategy is presented in a grid-connected microgrid. • The microgrid under consideration consists of PV and 174 vehicle chargers. • The controller's strategy minimizes power grid peaks and maximizes EV charge energy provided by the PV cells. • The controller was integrated and implemented in the microgrid system. • An average daily grid peak power reduction of 59% was achieved over a three week period. [ABSTRACT FROM AUTHOR]

Under the background of renewable energy infiltration, the generation-grid-load-storage type integrated system presents the double-side randomization characteristics. The multi-time scale interactive response mechanism of multiple subsystems is complicated. The effective dynamic control method is important for the efficient and stable operation of the integrated energy system. In this paper, dynamic characteristic analysis and simulation experiment study of the integrated system under double-side randomization conditions are carried out. First, a multi-time scale dynamic response model of the integrated system coupled with millisecond level on the electrical side and kilo-second level on the thermal side is developed. Second, based on the frequency hierarchical idea, an algorithm of subsystems cooperation is proposed. It is suitable for different distributed energy systems and can be applied to both day-ahead control and intra-day control. Third, the dynamic characteristics of the integrated system under double-side randomization conditions are analyzed. Working conditions include sunny day, cloudy day and rainy conditions on the generation side and industrial load, commercial load and appliance load conditions on the load side. Finally, based on the dynamic experimental platform of the generation-grid-load-storage type integrated system designed and built by our research group, experiment studies are carried out. The results present that the frequency hierarchical control method can reduce the power purchase and abandonment rate. The average of them is reduced by 1.76%, 10.47% and 11.93% under different loads. The maximum error between experimental data and simulation data is 12.32%. The switching process of the vanadium redox flow battery charge and discharge is the main reason of errors. [Display omitted] • A multi-time scale dynamic response model of integrated energy system is developed. • The frequency hierarchical control method of integrated energy system is proposed. • The system performance under double-side randomization conditions are analyzed. • The control method is verified on an experimental platform with dynamic real-time load. [ABSTRACT FROM AUTHOR]

Low-carbon operation of renewable microgrids (MGs) is challenging because of the large share of distributed renewable energy sources (RESs) and increasingly complex loads. Therefore, a favorable energy management system (EMS) should be developed. In this paper, in order to promote the grid-friendly and economic interaction with the main grid, an improved multi-time-scale EMS is proposed for the renewable MGs composed of distributed RESs, battery energy storage (BES) units and flexible loads. Firstly, flexible resources, such as BES units and flexible loads, are integrated to deeply participate in the low-carbon operation of renewable MGs. A dynamic and precise operating reserve scheme based on the probabilistic interval is formulated to ensure that BES units can operate in a coordinated manner between economic scheduling and operating reserve. Then, the demand respond based on the expected kilowatt-hour power curve variance and power fluctuation cost model is constructed for jointly regulating the distribution of the tie-line power. Last, the improved multi-time-scale EMS consists of day-ahead scheduling plan and intraday rolling correction. The intraday rolling scale with a varying rolling window performs optimal rescheduling for remaining periods. The deviation between the day-ahead scheduling plan and actual operation is corrected in real time. Simulations on a park-scale MG revealed the effectiveness of the proposed multi-time-scale EMS. In particular, when the fluctuation coefficient was set to 0.1, compared with the existing day-ahead scheduling plan scenario, the peak-to-valley difference in the proposed method decreased by 62.262%, and this method requires a 2.072% increase in the grid energy cost. Moreover, in terms of actual operation results of two EMS cases, the total operating cost reduction in the proposed multi-time-scale EMS is 2.474% compared with the single-time-scale EMS case. The proposed multi-time-scale EMS case has superior fluctuation metrics, which shows that the interactive operation of in the proposed multi-time-scale EMS is more grid-friendly. [Display omitted] • Flexible resources are deeply participated in the low-carbon operation of microgrids. • A precise operating reserve scheme based on the probabilistic interval is formulated. • The expected power curve variance model is proposed to regulate the tie-line power. • The improved multi-time-scale EMS performs optimal rescheduling for remaining periods. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources may result in more severe active power disturbances, and requires more load shedding. The battery energy storage system (BESS) characterized with high flexibility can handle such uncertain disturbances and reduce load shedding amount. This paper proposes a chance-constrained optimal sizing scheme of the BESS to coordinate with the synchronous generator and emergency load shedding (ELS) for frequency stability. First, considering the role of the synchronous generator, BESS, and ELS in frequency stability control, an extended system frequency response model is established to calculate the amount of load shedding, and the required capability of frequency regulation. Then, a multi-objective and chance-constrained mixed integer nonlinear programming (MINLP) model is built for the BESS sizing to minimize investment cost and maximize operation profits, which considers the uncertainties of disturbances. With the utilization of the linear weighted method, max-affine function-based piecewise linearization method and Bernstein approximation, the model is reformulated into a tractable form, which is iteratively solved by the multi-cut Benders decomposition. Case studies were carried out to verify the effectiveness of the proposed scheme, showing superior performance in reducing load shedding amount and improving frequency nadir. • A hierarchical optimization framework for the BESS sizing to coordinate with synchronous generators and ELS. • An estimation method for the required frequency regulation capability of the BESS and the amount of ELS. • A chance-constrained and multi-objective MINLP model for the BESS sizing with coordinated frequency control and uncertain disturbances. • Reformulate the chance-constrained and multi-objective model into a tractable MILP form. [ABSTRACT FROM AUTHOR]

The uncertainty of wind power poses a serious challenge in peak regulation for multi-area interconnected power system, and this paper proposes a method for quantitatively assessing the peaking resource demand of a multi-area interconnected power system, which takes into account the complementary characteristics of inter-area regulating resources and the wind power forecast error. First, wind power historical data are analysed with a data-driven model-based wind power forecast error, to reduce the impact of renewable energy uncertainty on power system peaking demand. Subsequently, a multi-area peaking demand analysis method considering power flow constraints, which is proposed for the effective utilization of peaking resources in areas with complementary characteristics. The method can adequately quantify the area power margin, adjustable power, and area grid peaking demand. On this basis, a two-layer assessment model of multi-area peaking resource demand is constructed, the areas are divided into different categories of peaking aggregation based on the area peaking characteristics. The upper layer takes the area operating cost as the optimisation objective, to derive the optimal output of each area in the system, and the lower layer ensures the optimal matching of power among clusters, which is solved by using a Nash equilibrium. Finally, the effectiveness and superiority of the method is verified by case analysis, and the numerical results show that compared with the traditional global optimal method, the cost of operation and transmission has been decreased by 21.5% and the security of power flows has been significantly enhanced. • proposes a methodology for assessing peaking resources that incorporates a two-tier optimization structure. • propose a methodology for assessing the peaking demand of a multi-regional interconnected system, on the basis of analysing the characteristics of regional peaking. • propose a method to consider the uncertainty of new energy and the constraint of power flowt simultaneously. • propose an analysis method for the power system's demand for peak shaving resources under different power flow constraint margins. [ABSTRACT FROM AUTHOR]

Nowadays, due to the large integration of converter-interfaced renewable energy sources (CI-RES), distribution networks progressively transform into active components of power systems, i.e., they transform into active distribution networks (ADN). In this new environment, ADN provide inertia and primary frequency response (PFR) as ancillary services (AS) to the transmission system. Under this new operational paradigm, conventional modeling approaches fail to simulate and analyze accurately the dynamic behavior of modern ADN under large frequency disturbances. Therefore, the scope of this paper is to formulate a new aggregated dynamic equivalent model for the representation of ADN in frequency stability studies. The proposed model receives as input the grid frequency and provides as output the active power variation of the examined ADN. The proposed model consists of several branches, allowing for the modeling and analysis of distinct frequency AS such as inertia and PFR. The performance of the proposed model is cross-validated using frequency responses, recorded during the system split event, occurred on the 8th of January 2021 in Continental Europe. Comparisons with conventional modeling approaches are also performed, highlighting the superior performance of the proposed aggregated equivalent. • Derivation of aggregated equivalents for active distribution networks (ADN). • Provision of inertia and primary frequency response from ADN. • Representation of ADN in frequency stability studies. • Assessment of conventional load models for dynamic analysis of ADN. • Models' evaluation using measurements recorded during large frequency disturbances. [ABSTRACT FROM AUTHOR]

The integration of 5G base station (5G BS) clusters and edge data services introduces novel digital loads (NDLs) into the distribution system (DS), significantly impacting the interactive coordination of 5G-DS. This paper proposes an expansion planning model of 5G and DS considering source-network-load-storage coordination. Here, renewable energy resources (RESs), 5G BS clusters, edge data centers (EDCs), and DS source-network-load-storage are coordinately planned. The dynamic service access of 5G BS cluster are formulated to capture the coupled nature and inherent flexibility of 5G services. Considering the flexible processing of EDCs, a detailed categorization is conducted based on data service type, leveraging the synergies between cloud and edge computing to achieve flexible data load distribution. The original expansion planning model is reformulated as a mixed integer linear programming (MILP) problem to facilitate its computational solution. Simulation results from case studies on Portugal 54 node network over individual expansion planning and 5G static-access are performed to validate the effective and superior performances of the proposed method. Simulations results show that the system cost can be reduced by at most 15% with a higher operational flexibility. [Display omitted] • An expansion planning model of 5G and DS considering source-network-load-storage coordination is proposed. • NDLs from 5G BS clusters and EDCs are introduced to accommodate the RESs. • Dynamic access services of 5G BS clusters are formulated to leverage their flexibility. • Synergies of cloud and edge computing in EDCs are captured to facilitate source-network-load- storage coordination. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

Since a renewable energy is connected to a high-altitude integrated energy system (HAIES), challenges arise for system operation. Shared energy storage as a jointly operated energy hub for multi-integrated energy system (IES) can effectively improve the economy and flexibility of the system. This paper proposes a joint day-ahead and intra-day scheduling strategy for a HAIES considering a shared composite energy storage operator (SCESO) and profit clearing scheme. First, a HAIES structure suitable for residential, industrial, and commercial applications is built based on combined oxygen supply and integrated demand response (IDR). An SCESO is then proposed to provide multiple energy sharing services for HAIES cluster, including electricity–oxygen–hydrogen. Accordingly, HAIESs and SCESOs are aggregated to create a HAIES cluster energy sharing framework. Second, during the intra-day scheduling stage, which is based on real-time forecast data that considers multiple source–load–charge uncertainties, the flexible adjustment capabilities of each unit of the framework are used to achieve intra-day energy balance and ensure the safe operation of the framework. Furthermore, a profit clearing scheme is proposed, introducing different energy contribution quantification methods for different energy cooperation modes. Finally, day-ahead and intra-day simulation results under multiple scenarios are analyzed. The simulation results demonstrate that the framework is optimal in terms of economy, adaptability to different RE outputs and RE uncertainties, and energy storage utilization efficiency. • It proposes a new HAIES and SCESO energy cooperation framework. • An intra-day scheduling strategy for the framework is proposed. • A profit clearing scheme is constructed. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out. [Display omitted] • The global LCOE for a typical REC has been defined. • The uncertainty propagation in the LCOE formulation has been studied. • The proposed model has been applied to an electrical polygeneration REC. • As an example, the Spanish and Italian regulatory frameworks have been considered. • The LCOE of RECs is mainly sensitive to the electricity price and the power load. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]

On the social index of access to clean energy, the country of CHAD is one of the least electrified in the world and the last in Sub-Saharan Africa (SSA), with only 11.3% of its population having electricity access. The access is considerably lower in rural areas of CHAD (1.3%), meaning that 98.7% of the Chadian rural population currently depends on the unsustainable and polluting fuels such as wood and charcoal to meet their energy needs. The absence of electricity is also an opportunity to find a new solution for sustainable, clean, affordable and reliable electricity, which is a prerequisite for social and economic growth. This paper proposes an approach for performing a techno-economic, environmental, and social assessment based on optimal modeling of PV/wind/battery/fuel cell systems in both connected to the grid and standalone configurations for delivering electricity to rural areas within the context of CHAD using MATLAB R2023b to implement and run two (2) meta-heuristic optimization methods: Non-dominate Sorting Genetic Algorithm and Cuckoo Search Algorithm. To fulfill this purpose, we used 2 study cases in two (2) regions of the Sahelian zone of CHAD (MASSAKORY in the HADJER-LAMIS region and MAO in the KANEM region) with geographical coordinates of 13°00′N 15°44′E and 14° 7′ 16" N et 15° 18' 37" E, respectively. The hourly load profile data for a whole year, the hourly metrological data and the characteristics techno-economic of the proposed system components are utilized for the same purpose. The Annualized System Cost (ASC) and the System Self-Sufficiency Index (SSSI) were chosen as objective functions. The simulation results demonstrate that the Net Present Cost (NPC) of proposed systems are $2,107,900 (grid connected), $ 512,650 (off-grid), the cost of the grid is $2100, the LCOE is 0.1457$/kWh (grid connected), 0.0538$/kWh (off-grid), and the LCOH is 2.2892 US $/kg. These last two criteria are the lowest in comparison to the findings of previous work conducted in CHAD by HOMER Pro. The yearly equivalent avoided Greenhouse gas (GHG) is 460,480 kg (grid connected system), 463,467 kg (off-grid system), the grid GHG emission is 3400 kg and the renewable energy sources fraction is 99.27%. Each of proposed systems will create four (4) new jobs and improve the HDI by 5.93% (grid connected system HDI = 0.4216) and 25.04% (off-grid system HDI = 0.4991). This study could help designers, companies, investors, policymakers, and the government of CHAD in decision-making during the installation of such a system in specific rural areas and also provide a better practical energy design tool. • A multi-criteria optimal sizing of an off-grid and grid-connected hybrid photovoltaic-wind system with battery and fuel cell storage system was proposed to give access to sustainable, affordable, reliable, and clean energy for rural electrification in CHAD. • Two (2) well-known metaheuristic optimization algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Cuckoo Search Algorithm (CSA), are implemented in MATLAB 2023b to size the proposed systems optimally. • A techno-economic, environmental, and social analysis was performed for the optimization of the systems sizing considering the two (2) configurations (grid-connected and off-grid) and two study cases in two (2) regions of the Sahelian zone of CHAD. • Results showed significant social, environmental, economic, and system reliability improvement. [ABSTRACT FROM AUTHOR]

• Demand response is used to address the uncertainty of solar generation. • Forecast errors are modeled based on solar generation forecasts at different times. • Model Predictive Control is formulated, incorporating solar generation forecasts. • Demand response reduces the aggregator's individual imbalances. • Aggregator is not financially incentivized to reduce individual imbalances. The high level of uncertainty of renewable energy sources generation creates differences between electricity supply and demand, endangering the reliable operation of the power system. Demand response has gained significant attention as a means to cope with uncertainty of renewable energy sources. Demand response of residential and service sector consumers, when accumulated and managed by aggregators, can play a role in existing electricity markets. This paper addresses the question to what extent aggregator-mediated demand response can be used to deal with the impacts of the uncertainty of solar generation. Uncertain solar generation leads to imbalances of an aggregator. These imbalances can be reduced by shifting flexible loads, which is called demand response for internal balancing. The aim of this paper is to assess the impact of demand response from loads in residential and service sectors for internal balancing to reduce the imbalances of an aggregator, caused by uncertain solar generation. For this purpose, a Model Predictive Control model which minimizes the imbalances of the aggregator through load shifting is presented. The model is applied to a realistic case study in the Netherlands. The results show that demand response for internal balancing succeeds in reducing imbalances. Even though this is favorable from the power system's perspective, economic analysis shows that the aggregator is not financially incentivized to implement demand response for internal balancing. [ABSTRACT FROM AUTHOR]

• The paper provides review of models integrating RES in Generation Expansion Planning. • It classifies models based on the applied theoretical approach in three categories. • Optimization, general/partial equilibrium and alternative models. • It compares their characteristics, advantages/disadvantages and their suitability. • It contributes in better understanding on the expected outcomes of each methodology. The Generation Expansion Planning (GEP) stands as one as one of the most discussed topics within the academia and decision makers in the energy sector, especially related to meeting deep emission reduction targets. Every country, aiming at decarbonizing its economy, focuses on the application of policies that could enhance the penetration of Renewable Energy Sources (RES) in its power capacity mix. GEP is a complex task, combining techno-economic, financial, spatial and environmental characteristics. Several models are developed to model GEP, applying different methodological approaches. The underlying theory is very important as it might inherit bias in the resulted outcomes. The debate on the appropriateness of each methodology is increased, especially as projected outlooks deviate from reality. The paper aims to provide a review of the models employed to integrate RES in the GEP. The paper classifies models in three generic categories: optimisation models, general/partial equilibrium models and alternative models, not adopting the optimum integration of RES in the GEP. It provides insights on the characteristics, advantages and disadvantages of the theoretical approaches implemented, as well on their suitability for different aspects of the problem, contributing in the better understanding on the expected outcomes of each methodology. [ABSTRACT FROM AUTHOR]