Search

Abstract To promote peer‐to‐peer trading in a distribution system with the franchise owned by the concerned distribution company respected, a so‐called “source‐grid‐load‐storage” (SGLS) integrated project is promoted in China. Given this background, this paper proposes a multi‐stage joint optimization model to optimize the participating strategy for SGLS‐IPs in electricity energy and ancillary service markets. A multi‐energy flow park model is presented with electricity, gas, and heat included. A two‐stage model for optimal participating strategy of SGLS‐IPs in electricity and ancillary service markets is then presented. Through scenario analysis of a sample system, the attained revenue of an SGLS‐IP in different markets is evaluated. Additionally, a bilevel Stackelberg game model is introduced for internal electric vehicle clusters (EVCs) within an SGLS‐IP, with the upper level representing an SGLS‐IP, and the lower level representing EV users engaging in the game. The effectiveness of the model is validated through case studies. Simulation results demonstrate that reasonable market participation and pricing strategies contribute to the efficient allocation of resources within a SGLS‐IP and a win‐win outcome for both the SGLS‐IP entity and EV users.

Abstract Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers. However, most classical methods suffer from significant time-consuming, memory overhead, and computational complexity issues as the scale of the power system concerned increases. With rapid development of quantum computing technology, the combinatorial optimization method based on quantum computing has shown certain advantages in computational time over existing methods. Given this background, this paper proposes a quantum computing based power system fault diagnosis method with the quantum approximate optimization algorithm. The proposed method reformulates the fault diagnosis problem as a Hamiltonian by using Ising model, which completely preserves the coupling relationship between faulty components and various operations of protective relays and circuit breakers. Additionally, to enhance problem-solving efficiency under current equipment limitations, the symmetric equivalent decomposition method of multi-z-rotation gate is utilized. Furthermore, the small probability characteristics of power system events is utilized to reduce the number of qubits. Simulation results based on the test system show that the proposed methods can achieve the same optimal results with a faster speed compared with the classical higher-order solver provided by D-Wave.

Abstract To promote the profit of generation companies and reduce carbon emissions from the generation sector, a two‐layer decision‐making model based on the cooperative game is proposed. Based on the analysis of a renewable‐fossil energy generation alliance to participate in electricity‐carbon‐green certificate markets, a robust optimization model considering the price uncertainty in the electricity spot market is first established based on the information gap decision‐making theory as the upper part of a two‐layer optimization model. Subsequently, the clearing models of the electricity spot, carbon emission trading, and green certificate markets are established. This two‐layer optimization model is transformed into a single‐layer model based on the well‐established KKT conditions. A profit allocation model for the members of the generation alliance is then presented based on the Shapley value and an improved nucleolus kernel method. Finally, the effectiveness of the proposed model is demonstrated by the IEEE 14‐bus power system.

Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterward, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

In recent years, the global power industry has experienced rapid development, with significant advancements in the source, network, load sectors, and energy storage technologies. The secure, reliable, and economical operation of power systems is a critical challenge. Due to the stochastic nature of intermittent renewable energy generation and the coupled time-series characteristics of energy storage systems, it is essential to simulate uncertain variables accurately and develop optimization algorithms that can effectively tackle multi-objective problems in economic dispatch models for microgrids. This paper proposes a pelican algorithm enhanced by multi-strategy improvements for optimal generation scheduling. We establish eight scenarios with and without pumped storage across four typical seasons—spring, summer, autumn, and winter—and conduct simulation analyses on a real-world case. The objective is to minimize the total system cost. The improved pelican optimization algorithm (IPOA) is compared with other leading algorithms, demonstrating the validity of our model and the superiority of IPOA in reducing costs and managing complex constraints in optimization.

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term.

Abstract The carbon market plays a critical role in promoting the transition toward renewable energy sources and reducing greenhouse gas emissions in the electricity generation and transmission. Extant research has overlooked the dynamic bilateral causality that exists between electricity and carbon markets. Moreover, these studies have frequently treated the macroeconomic effect as exogenous. To bridge this research gap, this paper presents a holistic modeling framework that comprehensively captures the intertwined nature of electricity and carbon markets and their concomitant interactions with the overarching economy. The suggested modeling framework is an integration of three principal modules, namely, a carbon market, an electricity market, and economic system. This synergistic blend provides an exhaustive understanding of the entire market operation cycle. It offers detailed clearance rules, and most importantly, it adopts a macroeconomic systematic modeling approach for evaluating the impact emanating from the interconnected electricity and carbon markets. To illustrate the practicality and effectiveness of the proposed approach, a case study anchored on empirical data sourced from the electricity and carbon markets in China is conducted. The empirical findings underscore the fact that incorporating a green certificate market into the modeling framework can precipitate a reduction in greenhouse gas emissions. Additionally, the results indicate that expanding the scale of the green certificate market from 1.9% in 2021 to 33% by 2023 will increase the generation of green electricity by 10%.

Abstract Developing renewable energy generation‐rich power systems could contribute to achieving carbon neutrality. To ensure the secure and economic operation of power systems with high penetration of renewable energy, it is necessary to solve the problem of inefficient utilisation of demand‐side resources by the current electricity market mechanism. The metaverse, an emerging technology attracting widespread attention, is expected to efficiently solve this problem. The metaverse can be regarded as a virtual‐real interactive economic system built on advanced technologies such as blockchain, artificial intelligence, extended reality, avatars, and decentralised autonomous organisations (DAO). This paper first briefly introduces the concept, architecture, technologies, and features of the metaverse. Then, a metaverse‐based DAO for energy systems is proposed and the corresponding business model is explored. The Energy DAO utilises algorithms and user consensus combined with smart contracts to solidify organisational operation rules. In this way, it organises users to directly participate in multiple types of electricity markets and carbon markets, as well as behavioural data production and transactions. Finally, an Energy DAO example for demand‐side sources demonstrates how the Energy DAO could solve the problems of information asymmetry, information opacity, and incentive incompatibility in electricity market mechanisms.

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

Abstract Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence, particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.

Abstract Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

Abstract With the ever‐growing demand for electricity, fast development of intermittent renewable energy generation, and evolving electricity pricing mechanisms, different network tariff schemes are implemented in various countries to address emerging challenges in power system planning and operation as well as electricity market evolution. Given this background, a survey of current practices on dynamic network tariffs in some representative countries is first presented. Subsequently, key issues of dynamic network tariffs including prerequisite, implementation and effects are described. Finally, from the perspective of electricity consumers, distribution system operators and regulatory authorities, the challenges associated with the implementation of dynamic network tariffs are discussed.

With the continuous development of information technology, data centers (DCs) consume significant and ever-growing amounts of electrical energy. Renewable energy sources (RESs) can act as clean solutions to meet this requirement without polluting the environment. Each DC serves numerous users for their data service demands, which are regarded as flexible loads. In this paper, the willingness to pay and time sensitivities of DC users are firstly explored, and the user-side demand response is then devised to improve the overall benefits of DC operation. Then, a Stackelberg game between a DC and its users is proposed. The upper-level model aims to maximize the profit of the DC, in which the time-varying pricing of data services is optimized, and the lower-level model addresses user's optimal decisions for using data services while balancing their time and cost requirements. The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method, as well as the effects of the time-varying data service price mechanism on the RES accommodation.

In the electricity market environment, electricity price forecasting plays an essential role in the decision-making process of a power generation company, especially in developing the optimal bidding strategy for maximizing revenues. Hence, it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm. Given this background, this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted $K$-nearest neighborhood (WKNN) method and the Gaussian process regression (GPR) approach. In the first step, several predictors, i.e., operation indicators, are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators. In the second step, the outputs of the first step are regarded as a new predictor, and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach. The proposed algorithm is verified by actual market data in Pennsylvania-New Jersey-Maryland Interconnection (PJM), and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm. Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

To reform the electricity selling trading and standardize the electricity retail market, the optimal participating strategies of power generation companies and electricity customers in an electricity retail market under the spot electricity market mode are investigated. First, the influence of the external environment on power generation companies is considered, the dispatching sequence of power generation companies is optimized, and the profit models of power generation companies and power retailers, as well as the utility model of electricity customers are built. Second, an improved genetic algorithm (IGA) is applied to solve the formulated optimal participating strategies model for power generation companies and electricity customers, and the effect of IGA is compared with that of traditional genetic algorithm (GA), simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm. The simulation results show that the IGA algorithm has the advantages of fast convergence and saving electricity consumption in this paper. Finally, two examples are employed to demonstrate the feasibility and efficiency of the developed strategies. Both example 1 for presented method in this paper and example 2 for multiple retailers competing, the simulation results show that the interests of market competing entities (participants) can be well balanced. Furthermore, the advantages of power retailers acted as a guider in electricity retail market are revealed, and the credibility and security of the electricity market management system are maintained.

Domestic photovoltaic (PV) installations are increasing their spread due to decreased cost, environmental friendliness, and relatively easy installation. However, the massive domestic PV deployment can bring the LV network (LV) distribution network to its sustained operating limits, as overvoltage and congestion can arise. The overvoltage problems can emerge due to reverse power flows and congestion can be caused if the installed PV capacity is higher than the distribution line loading capacity. The limitations of the LV networks arise especially during the seasons when domestic power demand is minimal, and PVs are generating at their peak. The research presented here incorporates a real suburban LV network with fifteen residential users. The measured load and PV generation data for one year are used to carry out power flow simulations. Several congestion leveraging strategies such as battery energy storage system (BESS) incorporation, reactive power control (RPC), and curtailment of peaks are discussed.

Abstract Non‐intrusive load monitoring (NILM) is essential for understanding consumer power consumption patterns and may have wide applications such as in carbon emission reduction and energy conservation. Determining NILM models requires massive load data containing different types of appliances. However, inadequate load data and the risk of power consumer privacy breaches may be encountered by local data owners when determining the NILM model. To address these problems, a novel NILM method based on federated learning (FL) called Fed‐NILM is proposed. In Fed‐NILM, instead of local load data, local model parameters are shared among multiple data owners. The global NILM model is obtained by averaging the parameters with the appropriate weights. Experiments based on two measured load datasets are performed to explore the generalization capability of Fed‐NILM. In addition, a comparison of Fed‐NILM with locally trained NILM models and the centrally trained NILM model is conducted. Experimental results show that the Fed‐NILM exhibits superior performance in terms of scalability and convergence. Fed‐NILM out performs locally trained NILM models operated by local data owners and approaches the centrally trained NILM model, which is trained on the entire load dataset without privacy protection. The proposed Fed‐NILM significantly improves the co‐modelling capabilities of local data owners while protecting the privacy of power consumers.

Abstract The current electricity market trading mechanism mainly includes the bilateral negotiation transaction and centralized transaction, with defects such as information asymmetry, insufficient competition, and weak price discovery. Thus, a new double‐market parallel trading mechanism is proposed to improve the efficiency of the competitive electricity market in this paper. Regard the electricity market as a price‐searching market and divide into two sub‐markets composed of a price‐searching market for buyers and a price‐searching market for sellers. Market entities can trade synchronously in two sub‐markets as price searchers or bidders. Searchers are responsible for organizing transactions in line with the market clearing rule library, and bidders participate in transactions initiated by searchers. Under the parallel mechanism, the traditional trading mechanism is also applicable. the framework of the proposed mechanism is presented from four aspects, including the description, clearing process, operation mode, and linkage between two sub‐markets. Then, a market clearing model of the proposed mechanism is built with several traditional transaction modes as special cases of the presented double‐market parallel transactions. Finally, taking the centralized transaction as an example, results show that the proposed mechanism can effectively provide market entities more chances to win contracts, and enhance the market liquidity.

Abstract Bidirectional power flow is becoming increasingly commonplace in modern distribution systems with the integration Renewable Energy Sources (RESs) and Energy Storage Systems (ESSs). This has facilitated in bringing generation closer to end‐consumers, thus leading to the emergence and rapid growth of microgrids. Additionally, due to the technological revolution in Information and Communications Technology (ICT), innovative business models have developed where willing and able participants of the power system can participate in the electricity market. In line with this evolution of both the electric power system and its participants, Energy‐as‐a‐Service (EaaS) is emerging as an increasingly prominent business model to address electrical grid challenges economically. EaaS encourages electricity grid customers to play a more active role and participate in different electricity markets. This paper introduces and simulates a variant of a Transactive Energy (TE) trading algorithm for microgrids. The paper describes the model in brief how multiple microgrids assets can trade electricity to ensure more efficient and economical local resource utilization. The main motivations of this research paper is to validate and test the suitability of the TE trading algorithm for a variety of conditions. The coordinated TE trading model can optimize expansion and investment planning especially at the distribution level.

With the ever-increased installed capacity of renewable energy generation units in a power system, the so-called shared energy storage (SES), a novel business model under the umbrella of the shared economy principle, has the potential to play an essential role in the accommodation of renewable energy generation. However, unified evaluation standards and methods, which can help decision-makers analyze the performance of the SES market, are still not available. In this paper, an evaluation index system of the SES market is designed based on the trading rules of China’s Qinghai province and the structure-conduct-performance (SCP) analytical model. Moreover, the definition and characteristics of the indices, which can show the performance of the SES market from different perspectives, are given. Furthermore, the ideal cases are presented as the evaluation benchmark based on the development expectation of the SES market, and the analytic hierarchy process (AHP) and the technique for order preference by similarity to an ideal solution (TOPSIS) are applied to evaluate the SES market comprehensively. Finally, a case study based on actual data of the SES trading pilot project in Qinghai shows that the evaluation index system can reflect the operation status, existing problems and influencing factors of the SES market.

Abstract A more reliable, efficient, and resilient smart grid depends on the applications of advanced information and communication technologies to support new functions and controls. The critical infrastructure of a smart grid consists of some major components such as monitoring, controls, communication protocol and software. The cyber‐physical system (CPS), which integrates these components, is an important enabler for the expected transition of the energy system driven by decarbonization, digitalization and decentralization. This paper provides an overview of the policy drivers and barriers for the implementation of CPS in power systems. With the vast deployment of distributed energy resources (DERs), there is increasing demand to model the hardware, software and their interactions in the smart grid environment. This paper reviews the modelling and applications of intelligent CPS for decentralized energy systems. The integration of DERs and the supportive infrastructure make modern power systems more vulnerable and less reliable to external threats such as terrorist intrusion. There are growing concerns over the risk of cyber‐attacks in mart grids. This paper surveys the latest progress on critical infrastructure identification and protection, as well as risk assessment and mitigation methods for cyber‐attacks. Finally, some advanced issues in cyber‐physical energy systems are addressed.

The resiliency of a standalone microgrid is of considerable issue because the available regulation measures and capabilities are limited. Given this background, this paper presented a new mathematical model for a detailed photovoltaic (PV) module and the application of new control techniques for efficient energy extraction. The PV module employs a single-stage conversion method to integrate it with the utility grid. For extraction the maximum power from PV and integrate it to power grid, a three-phase voltage source converter is used. For obtaining the maximum power at a particular irradiance a maximum power point tracking (MPPT) scheme is used. The fuzzy logic control and adaptive network-based fuzzy inference system are proposed for direct current (DC) link voltage control. The proposed model and control scheme are validated through a comparison with the standard power-voltage and current–voltage charts for a PV module. Simulation results demonstrate that the system stability can be maintained with the power grid and in the island mode, in contrast with the MPPT.

Abstract The continuous growth of renewable generation in power systems brings serious challenges to electricity markets due to their characteristics different from conventional generation technologies. These challenges come from two dimensions, including short‐term (energy and ancillary service markets) and long‐term (long‐term bilateral and capacity markets) aspects. Under this background, the design of energy and ancillary service markets is studied for power systems with a high penetration level of variable renewable generation. In the proposed spot market mechanism, energy and frequency regulation service (FRS) bids are jointly cleared, where renewable generators are motivated to proactively manage the intermittency and uncertainty of their power outputs. The proposed market mechanism can also ensure the adequacy of FRS capacity for compensating variability of renewables. Besides, in order to ensure the execution of spot market clearing outcomes, this paper established a penalty scheme for mitigating the real‐time fluctuations of renewable generation outputs in the spot market. Differences between real‐time generation outputs and market clearing outcomes are managed within a certain limit by imposing the designed penalty prices on deviations. Finally, the feasibility and efficiency of the developed market mechanism and algorithms are manifested in the case studies.

A microgrid (MG) is always prone to the uncertainties of its demand variation and the generation of its non-dispatchable renewable sources when operating in islanded mode. Such variation events can lead to the voltage or frequency (VF) violation in the MG. However, there are some techniques available in the literature that can predict these events a few minutes ahead. Using such techniques, the VF violation can also be predicted and prevented with the introduction of a suitable preventive controller. Hence, this paper proposes a look-ahead controller that uses the short-horizon prediction data of demand and renewable generation to determine any prospective VF violation. If a violation is predicted, the proposed technique will aim to define the most optimal generation level of dispatchable sources, the MG’s best network configuration and the engagement level of the supportive actions, such as exchanging power with neighboring microgrids, utilizing energy storages, a demand response and renewable energy curtailment (if and when available). The technical, reliability and environmental aspects of the MG are considered within the proposed technique along with the operational cost. The determined optimal control variables are then sent to the local controllers to apply the proper arrangements in the system to retain the VF within the desired range. The performance of the developed technique is validated through extensive numerical analyses in MATLAB.

Aiming at the distributed resources of electric vehicles with photovoltaics (PVs) on the user side, a trading mode of surplus energy sharing for electric vehicles based on the user-side PVs is proposed by utilizing the bidirectional mobility of information and energy. Power transfer can be implemented between different electric vehicle users through vehicle-to-grid (V2G) technology with a reasonable distribution of benefits taken into account. First, the operational framework of electric energy trading is presented, and the transmission architecture of each body of interest in the system is analyzed. Second, the portraits of EV users’ charging behaviors are established considering their different charging habits, and electric vehicle users are divided into electricity buyers and sellers in each trading time period. An electricity transaction model based on “multi-seller–multi-buyer” is established, and all electricity transactions are realized through blockchain-based decentralized technology. Finally, the benefit to each interest group is maximized using the improved Northern Goshawk Optimization (NGO) algorithm. Simulation results of a sample system indicate that the new power trading mode proposed in this study could lead to reasonable reuse of the electric energy of private electric vehicles and can achieve a win–win situation for all stakeholders.

The increasing penetration of distributed energy resources (DERs) into distribution networks has changed the energy trading pattern in traditional electricity markets to some degree, and this will possibly cause network congestion and nodal voltage violations. This paper proposes a two-stage modeling framework for peer-to-peer (P2P) energy trading with voltage regulation services provision considered. In the first stage, direct P2P trading among prosumers, considering network congestion management, is enabled. In the second stage, prosumers provide voltage regulation services to address possible voltage violations. Aiming at maximizing social welfare, the alternative direction method of multipliers (ADMM) is applied to solve the two-stage problem. On the basis of the optimal energy solution of the two-stage problem, the energy prices of P2P transactions and the price of voltage regulation services are settled based on the Nash bargaining model. Finally, simulation results of the IEEE 33-bus power system with six prosumers included demonstrate the effectiveness of the proposed models.

Abstract Energy and reserve markets are two important market components in the deregulated power industry. Energy markets provide a platform for generating companies, retailers, and big energy consumers to centrally trade electricity. However, owing to the varying renewable power generation, load variation and unexpected contingencies, energy supply and consumption always embody high uncertainties. Furthermore, due to the lack of low‐cost, large‐capacity energy storage devices, an instant balance between the generation and load is required under these uncertainties. Therefore, the reserve market is set up to address the balance‐disruptive uncertainties intrinsic to the power grid. Although this combined market design reduces the risk of the possible imbalance in energy supply and consumption, it does not guarantee that the frequency of the grid is satisfactory, even in the normal operation. This study analyzes the insufficiency of the energy and reserve market design in addressing frequency quality problems by exploring the root causes for the deteriorating frequency quality in the Australian National Electricity Market. A novel framework that incorporates the frequency quality, control capacity, and control cost to achieve the desired frequency quality is then presented. Case studies through a simple power system and the IEEE 30‐bus system demonstrate the efficacy of the presented methods.

Abstract The ever‐increasing competition in electricity retail markets has become one of the pivotal causes for electricity retailers to provide attractive packages to consumers for enhancing their market shares. Based on the current user‐side time‐of‐use electricity price mechanism in China, a bi‐level optimization model for designing a time‐phased and quantity‐incentive electricity package is proposed for electricity retailers considering bounded rationality of consumers. In the upper‐level model, the optimization objective is to maximize the electricity purchase and sale revenue of a specified retailer. In the lower‐level model, consumers are modelled as bounded rational decision‐makers whose objective is to purchase the electricity package with the highest value, considering the value of the excess electricity of the package. In addition, consumer psychology theory is introduced to describe the bounded rational responses of consumers to electricity packages. Simulations based on data of residential consumers in a city of China illustrate that by properly determining package quotas in different time periods, electricity retailers can guide consumers to change consumption time and volume, and hence, increase their revenues by increasing trading volume or reducing electricity procurement costs in forward bilateral transactions and spot markets.

Abstract The merits of a virtual power plant in integrating photovoltaic generation and flexible loads, such as a chilled water thermal storage air conditioning system and an electric vehicle, are well recognized. However, the optimal operation of a virtual power plant is challenged by the complexities of solar irradiance and the large size of a chilled water thermal storage air conditioning system and an electric vehicle. This paper proposes a new approach to the optimal dispatch problem of a virtual power plant. The stochastic dynamic of solar irradiance is modelled by a stochastic differential equation set. The binary decision for a chilled water thermal storage air conditioning system and an electric vehicle are characterized by a mixed logical dynamical model. The resulting optimal dispatch problem is solved by the receding horizon approach. The appeal of the proposed approach is in its capability to consider the stochastically dynamical impact of solar irradiance. Besides, the proposed approach can solve the optimization problem over a relatively small period of time, and thus has the potential for online applications. Finally, the feasibility and effectiveness of the proposed approach is demonstrated by numerical simulations.

With the announcement of the carbon peaking and carbon neutrality target in China as well as the launch of the nationwide green power renewable generation trading, one of the key issues is how to design an effective renewable portfolio standard (RPS) and build an efficient tradable green certificate (TGC) market. The quota that stipulates the share of electricity supplied from qualified renewable energy (RE) sources has substantial impacts on the TGC market, the electricity market, and the occurred time of carbon emission peaking. However, few studies have been reported on effectively quantifying the impacts of quota variation. Given this background, this paper presents an innovative system dynamics (SD) model to evaluate the impacts of quota variation on the trading results of the TGC market and the trend of carbon emissions in China for the next decade. The proposed SD model takes factors such as the accommodating capability by the power system for RE generation, the policy-driven carbon emission reduction target as well as the decreasing fixed feed-in tariff (FIT) for RE generation as constraints for the first time. These factors are quantified in the constraints based on the current RE policies and market trading mechanism in China to improve the evaluation accuracy of the SD model. Using real-world data, simulations under various scenarios are carried out to validate the feasibility and efficiency of the proposed model. The methods for further improving the design of TGC market are also further explored, which are expected to effectively guide China to achieve the carbon peaking target.

Under the China’s ‘dual carbon’ national goal–reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060, one of the key issues in China is how to smoothly transit from a fixed-price mode to a competitive market pricing mode for renewable energy generation companies. Aiming at minimizing governmental subsidies and maximizing the fairness among renewable energy generation companies, a multi-agent three-layer transition mechanism with the transactions of green certificates considered is proposed in this paper. Through adjusting subsidy policies, the developed transition mechanism can stimulate the renewable energy generation companies to gradually participate in the competitive electricity spot market. Specifically, a multi-market multi-agent transaction framework in the transition mechanism is first established. Then, in order to derive the important parameters of the transition mechanism, a method that decouples the electricity market and the green certificate market is designed. Finally, the feasibility and efficiency of the proposed transition mechanism are demonstrated through numerical examples.

Abstract Due to the development of new technologies, change of generation mix and appearance of newly formed energy supply hubs, there is a large year‐on‐year change in the marginal loss factors in power systems. Since any change of marginal loss factors could have significant impacts on payment of loads and profitability of generators, it is necessary to carry out a comparative study on the loss factor‐based locational marginal pricing methods. Considering that a systematic comparison of various locational marginal pricing methods has not been reported in existing publications, this work presents a comparative study of the loss factor‐based locational marginal pricing methods that are widely adopted in electricity markets. Advantages and disadvantages of each locational marginal pricing method are explored in detail, and could serve as references in selecting appropriate locational marginal pricing methods in practice. The selected five locational marginal pricing models are tested in two standard power systems, that is, the IEEE 5‐bus and 39‐bus systems. Then, through numerical experiments and detailed analysis, key findings about the reference point dependency of loss factors, accuracy of loss estimation, load payment, generation income, and market settlement surplus are summarised and elaborated. It is found that marginal loss factors‐based locational marginal pricing methods tend to produce a higher market settlement surplus and can lead to a lower generation income than other locational marginal pricing methods.

Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand of end-users. Demand response (DR) has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies. The percentage of the air-conditioner (AC) load over the total load demand in a building is usually very high. Therefore, controlling the power demand of ACs is one of significant measures for implementing DR. In this paper, the increasing development of ACs, and their impacts on power demand are firstly introduced, with an overview of possible DR programs. Then, a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out. Next, comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented. Finally, the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems.

The interactions between randomly fluctuating power outputs from photovoltaic (PV) at the DC side and background voltage distortions at the AC side could generate interharmonics in the PV grid-connected system (PVGS). There is no universal method that can reveal the transmission mechanism of interharmonics and realize accurate calculation in different scenarios where interharmonics exist in the PVGS. Therefore, extended dynamic phasors (EDPs) and EDP sequence components (EDPSCs) are employed in the interharmonic analysis of the PVGS. First, the dynamic phasors (DPs) and dynamic phasor sequence components (DPSCs) are extended into EDPs and EDPSCs by selecting a suitable fundamental frequency other than the power frequency. Second, an interharmonic analysis model of the PVGS is formulated as a set of state space equations. Third, with the decoupling characteristics of EDPSCs, generation principles and interactions among the interharmonics in the PVGS are presented by the sequence components, and its correctness is verified by simulation and experiment. The presented model can be used to accurately calculate the interharmonics generated in the PVGS both at the AC and DC sides. Because of the decoupling among the EDPSCs, the set of state space equations can effectively describe the principle.

Abstract With ever‐growing electricity consumption, unwanted climate change, and evolving generation mix, some energy policies related to electricity markets are enforced in various countries to cope with emerging challenges in the energy sector. Electricity market regulation, which is required to keep pace with the continuous development of power industry restructuring and electricity market operation, is also evolving. To help researchers and policy makers have a better understanding of the state‐of‐the‐art electricity market regulation, the aim is to survey the latest progress, as well as the development trends in this field in the United States, Australia, United Kingdom, and France, which have mature and widely recognized electricity markets around the globe. Subsequently, the progress of the electricity market reform in China is described, and the status, drawbacks, and prospect of the electricity market regulation in China presented.

Abstract Alarm messages uploaded to a dispatch centre following the failure of a power system contain extensive temporal information. The accuracy and speed of fault diagnosis can be improved upon taking full advantage of such temporal information contained in these messages. From this standpoint, a power system fault diagnosis model based on time series similarity matching is proposed herein. First, a set of suspected faulty components can be determined after the occurrence of a fault or a set of faults. Then, on the basis of the specifications of protection devices, including protective relays and circuit breakers, and the defined time series model, a set of alarm hypothesis time series is generated, containing action process information of the protection devices involved in these suspicious components. Meanwhile, the alarm messages received by the dispatch centre are pre‐processed, and the alarm information time series are obtained. Subsequently, the fault component is obtained by calculating the similarity between each series in the alarm hypothesis time series set and the alarm information time series. Finally, the correctness and effectiveness of the proposed fault diagnosis model are demonstrated via a real‐life scenario in a local power system in China.

In the European renewable energy portfolio, wind has a sizeable share in the total energy production. The Nordic and Baltic energy systems in particular are benefiting from wind energy to reach the greenhouse gas emissions reduction objectives set by the EU. The wind energy production varies with time, and this intermittent characteristic imposes a challenge for full utilization of renewable energy potential. The power system operator needs to ensure timely power supply of demand. An accurate estimation of power output from a non-dispatchable generation resource such as a wind farm is essential for the operator to ensure the supply–demand balance and adequate sizing of reserve power capacity. Existing methods of feature extraction and prediction such as linear regression often overlook the significant variations or do not utilize in the model building. However, this method misinterprets the trend in data. Understanding the properties of the variations in more details would reduce the uncertainty and significantly improve the feature extraction to aid in decision making. Furthermore, as the volume, shape and type of dataset start to increase and new methods are required to extract meaningful information from the patterns in the big data. The objective of the paper is to present a novel Ramping BehaviourAnalysis (RBAθ) model that identifies and quantifies the variations in a time-varying dataset. The variations are classified into significant and stationary events. The former refers to the significant swings beyond a set threshold range and the latter refers to the swings that are relatively within the threshold limits. The features associated to each event include start time, end time, change in magnitude, persistence of an event, angle at which the event took place and frequency of occurrences of the features. In addition, the rain-flow cycles count is extracted from the original data for each event as a sum of half cycles and full cycles. The model is validated using simulated wind power production data from a virtual wind park spread across Estonia and the results are elaborated. The spatial dynamics of the virtual windfarm are captured through localized spatial autocorrelation of the events with the geospatial locations of the turbines. The results demonstrate that RBAθprecisely and accurately identify and quantify the time varying power generation into events with subsequent features. The volume of the data is significantly reduced in the process of summarizing time series data into a series of events. Thereby RBAθcan be also used for data compression and reconstruction with minor losses. The system operators can use the proposed algorithm in operational scheduling, maintenance and investment-capacity building decisions.

Abstract The flexible charging and discharging behaviours of electric vehicles (EVs) can provide a promising profit opportunity for EV owners or aggregators in deregulated market. However, a major barrier to overcome is the uncertain market prices. In this context, this study aims to establish a novel and robust model for EV aggregators to optimally participate in multiple types of electricity markets while considering the uncertainty of the prices in various markets. First, the electricity market framework and the obligation of an EV aggregator are introduced, and each EVaggregator is entitled to participate in day‐ahead, real‐time, and regulation service markets. To cope with forecasting errors of market prices and other uncertainties, a robust optimisation model is developed to schedule the charging/discharging of EVs in different markets. A stepwise bidding strategy is then proposed to formulate robust market bids, offering more flexibility in handling volatile market prices in actual electricity markets. After the clearing of multiple types of electricity markets, a real‐time dispatch model for EV aggregators is proposed for allocating the settlements and regulation orders to each EV. Finally, scenario‐based simulation is employed to demonstrate the effectiveness of the proposed bidding strategy using actual market data and regulation signals.

Abstract The concept of community solar projects (CSPs) implies immense technical and economic incentives, but has not been widely adopted because of the investment risks. In this context, solar leasing can be very attractive and can lead to significant uptakes of residential rooftop photovoltaic (RPV) systems. This paper proposes solar leasing as an option to realize a CSP for a multi‐housing residential complex. Under the proposed approach, a company will lease the roof of some residential premises for installing and operating RPV systems, and selling the generated electricity to the community and the utility. The key considered design parameters are the rating and penetration level of RPV systems, as well as the capacity and the charge/discharge rate of the employed battery energy storage systems, in addition to the number of the houses that have leased their roofs and the energy trading tariffs. The impacts of these parameters on supporting the utility feeder such as network peak demand shaving and flattening the duck curve profile, while achieving the required level of self‐sufficiency for the multi‐housing complex are studied. These performance indicators have been also analyzed with respect to the economic parameters of the investing company.

Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system.

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research.

Present trends indicate that electrical vehicles (EVs) are favourable technology for road network transportation. The lack of easily accessible charging stations will be a negative growth driver for EV adoption. Consequently, the charging station placement and scheduling of charging activity have gained momentum among researchers all over the world. Different planning and scheduling models have been proposed in the literature. Each model is unique and has both advantages and disadvantages. Moreover, the performance of the models also varies and is location specific. A model suitable for a developing country may not be appropriate for a developed country and vice versa. This paper provides a classification and overview of charging station placement and charging activity scheduling as well as the global scenario of charging infrastructure planning. Further, this work provides the challenges and solutions to the EV charging infrastructure demand and deployment. The recommendations and future scope of EV charging infrastructure are also highlighted in this paper.

With the extensive installation of intelligent electronic devices with bi-directional fault monitoring capabilities, richer fault direction information can be collected and utilized to achieve an accurate fault diagnosis. In this paper, we consider the fault diagnosis problem in active distribution systems with distributed generators connected, such as rotating electrical machine power sources and centralized inverter interfaced renewable energy resources. The fault diagnosis problem is modeled as a linear integer programming problem with an objective to minimize the numbers of fault zones and false alarms. A novel functional form is derived to capture the expected alarms sent by bi-directional fault monitoring devices to be compared with the actual alarms received by the dispatch center. Uncertainties in both the monitoring and communication stages are considered in the model by formulating the numbers of false alarms in the objective function. Three types of suspected false alarms can be detected: “missing alarms”, “distorted alarms”, and “reverse alarms”. By solving the developed optimization model for fault diagnosis, false alarms and suspected fault zones can be found. Case studies in a modified IEEE 33-bus system and a 55-bus system in Guangzhou, China are carried out in several different scenarios with multiple faults to demonstrate the performance of the proposed model. Numerical tests show that the proposed approach is superior in computational time such that it can be used for real-time fault diagnosis in active distribution systems.

The development of the concentrating solar power (CSP) plant as a new dispatchable resource that can participate in the electricity markets as an independent power producer and coordinate intermittent renewables has attracted much attention recently. In this work, optimal offering strategies of a price-taker CSP plant in the day-ahead (DA) and real-time (RT) electricity markets are addressed considering non-stochastic uncertainties (NSUs) from the thermal production of the CSP plant and stochastic uncertainties (SUs) from the market prices as well as the risk attitude of the CSP plant concerned. A hybrid stochastic information gap approach (SIGA) integrating the well-established information gap decision theory with the mixed conditional value at risk (CVaR) is proposed to hedge the revenue risk against NSUs and SUs in the offering problem based on the risk preference of the decision maker. A two-stage architecture is utilized for framing the DA and RT offering problems, where the first-stage co-optimizes offering strategies in the DA and RT markets, while the second-stage determines the actual RT hourly offering strategy in a rolling horizon manner. Case studies show that the SIGA can make optimal offering strategies against the non-stochastic thermal production and stochastic market prices given the risk attitude of the CSP plant. Comparisons also demonstrate that the SIGA could be an effective tool to manage coexistent NSUs and SUs.

With the target of carbon peaking and carbon neutrality, renewable energy generation (REG) develops rapidly. The increasing penetration of REG brings along the problems of fluctuation in power flow and the possible abandonment of wind and photovoltaics (PV) generation. In this context, the so-called integrated energy system (IES) becomes a promising solution to the accommodation of REG thanks to energy storage systems and coupling devices inside. In this paper, the optimal operation model of an IES is first presented, with the schemes of green certificate trading and carbon emission right trading included to provide economic incentives for accommodating REG. Next, in order to address the problem of uncertainty in REG, the devices in the IES are divided into three types based on regulation flexibility, and a multi-time period optimal dispatching scheme is proposed, including day-ahead optimal scheduling, rolling optimal dispatching, and real-time control strategy. Finally, it is demonstrated by simulation results of a numerical example that the proposed method not only promotes the accommodation capability for REG but can also cope well with contingencies.

During the transition towards a low carbon energy system, and as a result of the increasing penetration of renewable energy generation, ancillary services play an important role in ensuring the security and economics of power system operation. An integrated energy service provider (IESP), with the energy coupling and storage devices inside, can flexibly participate in electric energy markets and ancillary service markets. In this paper, a mixed-integer optimization problem is formulated to determine the optimal operating strategy for the IESP with ancillary service provision. First, the mathematical model of a regional integrated energy system (RIES) is established, including energy coupling devices and energy storage devices. Then, an optimization model for the IESP operation is formulated with the objective of maximizing daily operation profit, and the corresponding optimal capacity of ancillary service provision from the IESP is attained. Finally, case studies of an RIES are carried out to demonstrate the feasibility and effectiveness of the proposed method, and the impacts of the IESP with ancillary service provision on operational characteristics and economic benefits are analyzed.

In the context of modern power system development to support the evolution towards green energy and carbon-neutral emission goals, many existing problems and even challenges demand new technical solutions. In recent years, decentralized blockchain technology has been employed to address some problems in power systems, and many papers have been published. In this paper, the concept of blockchain is first introduced. A brief survey of the existing publications regarding the applications of blockchain in power systems, including power system dispatching, microgrid operation, energy trading, electricity trading settlement, transmission, and distribution system operation, is then carried out. In addition, several application scenarios of blockchain technology in power systems are also introduced. Through the discussion, we found that we still need to weigh the advantages and disadvantages, overcome its leakage, and bring its value into play if we apply blockchain technology in modern power systems in support of zero carbon goals.