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Liberalized electricity markets promise a cost-efficient operation and expansion of power systems but may as well introduce opportunities for strategic gaming for price-making agents. Given the rapid transition of today's energy systems, unconventional generation and consumption patterns are emerging, presenting new challenges for regulators and policymakers to prevent strategic behavior. The strategic offering of various price-making agents in oligopolistic electricity markets resembles a multi-leader-common-follower game. The decision problem of each agent can be modeled as a bi-level optimization problem, consisting of the strategic agent's decision problem at the upper-level, and the market clearing at the lower-level. When modeling a multi-leader game, i.e., a set of bi-level optimization problems, the resulting equilibrium problem with equilibrium constraints poses several challenges. Real-life applicability or policy-oriented studies are challenged by the potential multiplicity of equilibria and the difficulty of exhaustively exploring this range of equilibria. In this paper, the range of equilibria is explored by using a novel simultaneous solution method. The proposed solution technique relies on applying Scholtes' regularization before concatenating the strategic actor's decision problems' optimality conditions. Hence, the attained solutions are stationary points with high confidence. In a stylized example, different strategic agents, including an energy storage system, are modeled to capture the asymmetric opportunities they may face when exercising market power. Our analysis reveals that these models' outcomes may span a broad range, impacting the derived economic metrics significantly. [ABSTRACT FROM AUTHOR]

Vehicle to grid (V2G) is one the most important ways to effectively integrate electric vehicles (EVs) with electric power systems. The important benefits can be made by V2G such as reducing/increasing the cost/revenue of EV owners and technically sup-porting electric power systems. Concerning technical and regulatory constraints, EV owners must participate in electricity mar-kets via aggregators. This paper proposes a robust optimal coordinated charging (OCC) model including bidding ancillary ser-vices for regulation and spinning reserve markets. The presented work handles the uncertain behavior of the electricity market that are ancillary service prices and their deployment signals by the robust optimization approach. The aim of optimization is the maximization of the aggregator's profits from V2G by joining the ancillary services markets. The recommended robust OCC model which is a robust linear problem (RLP) model is simulated by the CPLEX solver in GAMS software. An assumed set of 10000 EVs in the electric reliability council of Texas (ERCOT) electricity markets is considered for doing simulations. Employing the presented model in this test system shows the efficacy of the proposed model in comparison to other deterministic and stochastic models. [ABSTRACT FROM AUTHOR]

In this paper, virtual power plant (VPP) planning is done using distributed generation sources to create a safe platform for electricity exchange and to increase the profitability and sustainability of electricity. In the proposed model, the effect of micro-grid interaction with the electricity market in the presence of distributed generation resources and storage is investigated. To solve this problem, an improved artificial bee colony algorithm using the accept-reject method (AR-ABC) is used. The AR method is employed to limit the initial search space as well as for the scenario reduction process. Also, uncertainties related to loads and renewable sources are formulated in a sample micro-grid including micro-turbine (MT), fuel cell (FC), wind turbine (WT), photovoltaic cells (PV) and batteries for storage; the results are compared with those of other methods, which shows this method works better than others. The software simulations of this research are done in the MATLAB software environment. [ABSTRACT FROM AUTHOR]

The evolution of the electricity market has brought the issues of market equilibrium and collusion to the forefront of attention. This paper introduces the Deep Deterministic Policy Gradient (DDPG) algorithm on the IEEE three-bus electrical market model. Specifically, it simulates the behavior of market participants through reinforcement learning (DDPG), and Nash equilibrium and the collusive equilibrium of the power market are simulated by setting different reward functions. The results show that, compared with the Nash equilibrium, collusion equilibrium can increase the price of nodal marginal electricity and reduce total social welfare. [ABSTRACT FROM AUTHOR]

The potential for hydrogen is high in industrial processes that are difficult to electrify. Many companies are asking themselves at what cost they can produce hydrogen using water electrolysis with hydrogen storage. This article presents a user-friendly and less computationally intensive method (called method 1 in the following) for determining the minimum of the levelized cost of hydrogen (LCOH) by optimizing the combination of electrolyzer size and hydrogen storage size and their operation, depending on electricity prices on the day-ahead market. Method 1 is validated by comparing it with a more accurate and complex method (called method 2 in the following). The methods are applied to the example of a medium-sized industrial company in the mechanical engineering sector with a total natural gas demand of 8 GWh per year. The optimized LCOH of the analyzed company in method 1 is 5.00 €/kg. This is only slightly higher than in method 2 (4.97 €/kg). The article shows that a very good estimate of the LCOH can be made with the user-friendly and less computationally intensive method 1. For further validation of the methods, they were applied to other companies and the results are presented below. [ABSTRACT FROM AUTHOR]

With the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system. [ABSTRACT FROM AUTHOR]

Hydrogen production through renewable energy-powered electrolysis is pivotal for fostering a sustainable future hydrogen market. In the electricity sector, hydrogen production bears an additional demand that affects electricity price, and mathematical models are needed for the joint simulation, analysis, and planning of electricity and hydrogen sectors. This study develops a Cournot and a perfect competition model to analyze the links of the electricity and hydrogen sectors. In contrast to other solving methods approaches, the Cournot model is solved by convex reformulation techniques, substantially easing the resolution. The case studies, focusing on the Iberian Peninsula, demonstrate the region's potential for competitive hydrogen production, and the advantages of perfect competition to maximize the use of renewable energies, in contrast to Cournot's oligopoly, where the exercise of market power raises electricity prices. Sensitivity analyses highlight the importance of strategic decision-making in mitigating market inefficiencies, with valuable insights for stakeholders and policymakers. • A Cournot and perfect competition models to analyze the electricity and hydrogen markets are proposed. • Convex reformulation techniques are used for efficient model resolution. • Renewable energy is maximized under perfect competition, while market power exercised under Cournot leads to higher prices. • Case studies on the Iberian Peninsula demonstrate the region's potential for competitive hydrogen production. [ABSTRACT FROM AUTHOR]

The electricity market in Spain, as in many European countries, is organized into daily, intraday, and reserve markets. This project aims to predict the supply curves in the Spanish intraday market that have six sessions with different horizons of application, using information from the market itself. To achieve this, we approximate these curves using a non-uniform grid of points and evaluate the quality of these approximations with a weighted distance, both based on empirical market data. We employ neural network models, including multilayer perceptrons (MLPs), convolutional neural networks (CNNs), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and a Transformer network alongside a naive model for benchmarking. The MLP and CNN models demonstrated significant improvements in predicting these supply curves for the six market sessions. [ABSTRACT FROM AUTHOR]

This study aims to carry out a techno-economic analysis of different hydrogen supply chain designs coupled with the Swedish electricity system to study the inter-dependencies between them. Both the hydrogen supply chain designs and the electricity system were parameterized with data for 2030. The supply chain designs comprehend centralised production, decentralised production, a combination of both, and with/without seasonal variation in hydrogen demand. The supply chain design is modelled to minimize the overall cost while meeting the hydrogen demands. The outputs of the supply chain model include the hydrogen refuelling stations' locations, the electrolyser's locations and their respective sizes as well as the operational schedule. The electricity system model shows that the average electricity prices in Sweden for zones SE1, SE2, SE3 and SE4 will be 4.28, 1.88, 8.21, and 8.19 €/MWh respectively. The electricity is mainly generated from wind and hydropower (around 42% each), followed by nuclear (14%), solar (2%) and then bio-energy (0.3%). In addition, the hydrogen supply chain design that leads to a lower overall cost is the decentralised design, with a cost of 1.48 and 1.68 €/kgH 2 in scenarios without and with seasonal variation respectively. The seasonal variation in hydrogen demand increases the cost of hydrogen, regardless of the supply chain design. • Techno-economic study of Hydrogen supply chains coupled with electricity system. • Centralized, decentralised, and hybrid hydrogen production models are designed. • Optimal location, sizing and operational schedules are determined. • Decentralised design offers cost efficiency in both demand scenarios. [ABSTRACT FROM AUTHOR]

Massive and diversified distributed energy resources (DERs) have great potential for improving the flexibility of power system operations. However, given the wide distribution, small capacity, and variable ownership subject characteristics of DERs, virtual power plants (VPPs) are suitable for aggregating the flexible DERs for participation in the wholesale energy market. Extant studies have focused on obtaining bidding strategies in multiple electricity markets. However, there are shortcomings in the relevant market mechanisms and temporal relationships for participating in energy and ancillary service markets. This work proposes a joint market temporal rolling bidding strategy for VPPs in the wholesale market based on real unified electricity market mechanisms in the southern region of China. Accordingly, the proposed method enhances the market-oriented operational profits of VPPs. [ABSTRACT FROM AUTHOR]

Assessing market power in the presence of different production technologies such as renewable energies, including wind and solar power, is crucial for electric market analysis and operation. This paper investigates structural market power by incorporating wind farms and solar generation over a short-term period. The study examines the issue of market concentration boundaries to assess structural market power by calculating the minimum and maximum market concentration index values in the day-ahead market. It models the technical specifications of power plants, such as the maximum and minimum production limits, ramp-up and ramp-down rates, and minimum required up and down times. By extracting the spatiotemporal correlation of wind power generation from real data, the uncertainty of renewable power generation is represented through a set of scenarios. The analysis explores the correlation effects of wind farms, solar generation, and wind penetration levels under different ownership structures. Simulation results using a modified PJM five-bus system illustrate the effectiveness of the developed method. Our results indicate that integrating renewable energy can reduce the Herfindahl–Hirschman Index (HHI) by up to 30% as wind penetration levels rise from 0% to 40%, fostering a more competitive market structure. However, the correlation between wind farms also increases market volatility, with the standard deviation of the HHI rising by about 25% during peak load periods. These findings demonstrate the practical applicability of the developed methodology for assessing market dynamics in the presence of renewable energy sources. [ABSTRACT FROM AUTHOR]

Abstract With the introduction of decarbonization policies worldwide, renewable energy, especially wind and solar power, is gradually taking a significant share in the power system. The two‐stage stochastic optimization methods are generally adopted to address the uncertainty issues and the two‐stage stochastic market clearing models are established with the participation of renewable energy. However, most existing two‐stage stochastic market clearing models usually intuitively presume the offering quantities of renewable energy are equal to their forecast values, neglecting the potential impacts of the strategic offerings on the market results and thus affecting the secure operation of power systems. To fill the gaps, this paper incorporates the strategic offering models of renewable energy into the two‐stage stochastic energy and reserve market clearing model. Within the two‐stage framework, the first day‐ahead stage serves for energy and reserve market clearing, while the second real‐time stage considers the adjustments on offering quantities of renewable energy according to the fluctuations of renewable generations. The two‐stage stochastic market clearing model is formulated as a mixed‐integer non‐linear programming problem, and an iterative algorithm is devised to obtain market clearing results after proper linearizations. The effectiveness of the proposed method is validated using the extended IEEE‐118 test system.

The integration of electricity grids with technologies for small-scale distributed generation based on natural gas consumption is one of the most important innovative directions for improving the reliability and energy efficiency of power systems. Synchronous operation of large energy consumers and distributed generation systems is a new element of the electricity system. This element is an industrial microgrid, which has a unique ability to control the operating modes of the electrical energy system compared to conventional industrial electricity consumers. The aim of this study is to develop a theory and methodology for the use of microgrids in industrial areas under the market-based conditions of electricity and natural gas purchase. An analysis of trends in the use of small-scale distributed generation shows that the operation of distributed generation based on renewable energy sources is not economically feasible in all countries. In some countries, such as Russia, for example, the use of distributed energy sources powered by natural gas is the most important. The study of pricing principles for the purchase of energy in the wholesale and retail electricity markets and for the purchase of natural gas from regional suppliers and on commodity exchanges shows that managing the volatility of energy demand schedules can have a significant impact on the prices of electricity and natural gas used by industrial companies and large consumers of energy resources. As part of a unified power system, industrial enterprises and small distributed generation systems simultaneously consume electricity from the Unified Energy System, generate electricity through a distributed generation system into the internal network of the enterprise and external network the power system, and consume natural gas through the enterprise consumption system and the distributed generation system. Synchronised operation of an industrial enterprise and a small system of distributed generation system in the unified control system of a microgrid enables the enterprise to reduce the combined costs of purchasing energy resources and increase the efficiency and reliability of power supply to equipment. The article presents a system of factors influencing the demand of microgrids for electricity and natural gas consumption and develops a model for the integrated control of industrial microgrids under their integration with the technology for managing the demand of industrial enterprises for electricity and natural gas consumption. The control algorithm developed for a microgrid takes into account the cross effects of changes in its energy and gas demand, energy market price factors, internal constraints for industrial load management, external external of the Unified Energy System and the Unified Gas Supply System, and the ability to meet the energy demand of external consumers of the microgrid. The paper is of scientific and practical importance and can be used in the process of developing, implementing and managing microgrids at industrial enterprises in Russia and around the world.

As the world's second largest producer of natural gas, Iran relies heavily on natural gas for 70% of its energy needs. There are, however, challenges associated with energy supply in remote regions attached to the gas pipeline network, particularly during the cold season when fuel oil is required for heating and energy provisioning. To address this issue, this research proposes the use of alternative energy sources, such as hydrogen, for non-residential energy consumption, including agriculture and livestock. A key objective of this study is to develop an optimization model for the generation of electricity from hydrogen resources, as well as formulate a robust strategy to assist electricity producers in maximizing their profits. Using this model, producers are able to set prices in accordance with their risk tolerance and present them to the market, thus mitigating price fluctuations. In the targeted sectors, the results indicate that appropriate pricing models can prevent rebound effects. Specifically, the implementation of this model can reduce natural gas consumption by up to 20% and fuel oil usage by 15% during peak seasons. Additionally, the optimal pricing strategy can lead to a 10% reduction in greenhouse gas emissions. The results of this research provide stable and reliable solutions to the energy supply challenges in these regions, thus making the energy market more resilient and environmentally friendly. • Study proposes hydrogen for non-residential energy, aids pricing stability. • Optimize electricity generation from hydrogen; maximize producer profits. • Mitigate price fluctuations with producer-tailored pricing strategies. • Target sectors show pricing models prevent rebound effects. • Stable solutions enhance energy resilience and reduce emissions. [ABSTRACT FROM AUTHOR]

Insufficient flexibility is a major barrier to the development of new power systems. Leveraging the resource allocation function of the electricity market is a promising way to enhance the flexibility of power systems and promote the consumption of renewables. The reasonable allocation of ancillary service costs plays a pivotal role in this function. Towards the target of "who causes, who shares," various research related to cost allocation has been conducted. However, there is a lack of quantitative analysis of the impact of different cost allocation mechanisms on the market participants' revenues. Whether various cost allocation mechanisms can alleviate the insufficient flexibility problem of power systems needs to be validated. With this in mind, taking operating reserve ancillary services as an example, a long-term market operation simulation model with energy-reserve joint clearing is established in this paper based on the time series production simulation. According to this, the revenues of market participants under different reserve cost allocation mechanisms are quantified. Besides, a self-dispatch model for the energy storage (ES) equipped by renewables is established, based on which the impact of ES on the revenues of renewables under different cost allocation mechanisms is analyzed. Case studies based on practical data from a provincial power grid in China demonstrate that with the well-designed reserve cost allocation mechanism, the revenues of flexible resources can be ensured. Meanwhile, renewables are incentivized to reduce their fluctuations and uncertainties by equipping the ES. Hence, the insufficient flexibility problem of power systems can be alleviated from both supply and requirements perspectives. [ABSTRACT FROM AUTHOR]

The electricity sector plays a key role in achieving zero emissions targets. The required transition will lead to substantial changes in the supply, demand, and distribution of electricity, as well as in stakeholder roles. Future market designs may change substantially to accommodate these changes, address challenges, and take advantage of new opportunities. This paper reviews the characteristics of future carbon-neutral electricity systems and electricity market design options. To provide a guiding framework for the literature review, we transfer the complexity of electricity systems into a three-layer structure: Firstly, we analyze papers that rely on techno-economic modeling of the physical electricity system. As a case study, we analyze various studies focusing on a decarbonized European electricity system in 2050. Secondly, we review papers that investigate the economic behavior and effects of self-interest-seeking stakeholders such as producers, network operators, and consumers. Finally, we review papers focusing on policy and market design questions that guide policymakers in achieving a target physical asset combination while considering the behavior of stakeholders. We highlight common trends and disagreements in the literature, review the main drivers of future markets, and finally provide a mapping between those drivers, challenges, and opportunities. The review concludes that the most promising next step toward a fully comprehensive assessment approach is to combine existing approaches across topical and disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Green hydrogen or renewable hydrogen is promising cause it has no carbon emissions in production process, which is water electrolysis with electricity from renewables. Although grids can ensure stable and enough electricity supply, it is difficult to determine whether the grid electricity for hydrogen production is from renewable sources. This article uses EU rules to identify green hydrogen produced by grid electricity and analyse the production. European grids are classified according to the calculated indexes regarding renewables penetration and electricity carbon emission intensity. The negative correlation between green hydrogen production and electricity prices in different time scales and geographical distribution is concluded. Using imported electricity can improve the potential and diversity of green hydrogen production. Nordic countries and Albania have the most potential of green hydrogen production using grid electricity. The results can be a reference for the electricity market on flexibility supply and the future green hydrogen market development. • Nordic countries and Albania have the most green hydrogen production by grid power. • Other bidding zones with lower electricity prices have more green hydrogen production. • Green hydrogen production is negative correlated with hourly electricity prices. • Using imported electricity can improve the production potential and diversity. • Green hydrogen production by grid power can provide flexibility to electricity market. [ABSTRACT FROM AUTHOR]

As the Chinese government proposes ambitious plans to promote low-carbon transition, energy storage will play a pivotal role in China's future power system. However, due to the lack of a mature electricity market environment and corresponding mechanisms, current energy storage in China faces problems such as unclear operational models, insufficient cost recovery mechanisms, and a single investment entity, making it difficult to support the rapid development of the energy storage industry. In contrast, European and American countries have already embarked on certain practices in energy storage operation models. Through exploration of key issues such as investment entities, market participation forms, and cost recovery channels in both front and back markets, a wealth of mature experiences has been accumulated. Therefore, this paper first summarizes the existing practices of energy storage operation models in North America, Europe, and Australia's electricity markets separately from front and back markets, finding that perfect market mechanisms and reasonable subsidy policies are among the main drivers for promoting the rapid development of energy storage markets. Subsequently, combined with the actual development of China's electricity market, it explores three key issues affecting the construction of cost-sharing mechanisms for energy storage under market conditions: Market participation forms, investment and operation modes, and cost recovery mechanisms. Finally, in line with the development expectations of China's future electricity market, suggestions are proposed from four aspects: Market environment construction, electricity price formation mechanism, cost sharing path, and policy subsidy mechanism, to promote the healthy and rapid development of China's energy storage industry. [ABSTRACT FROM AUTHOR]

In the current model, the unclear and unreasonable method of revenue sharing among wind-solar-storage hybrid energy plants may a lso hinder the effective measurement of energy storage power station costs. This lack of clarity discourages energy storage from effectively collaborating with renewable energy stations for green power trading and spot trading. Therefore, this study proposes an optimal revenue sharing model of wind-solar-storage hybrid energy plant under medium and long-term green power trading market to facilitate the coordinated operation and equitable revenue allocation. Firstly, a method for decomposing transaction volume of green power is introduced by considering the uncertainty of spot market prices and physical delivery characteristics of green power trading. Then, a coordinated scheduling strategy of hybrid renewable energy plant is proposed to maximize revenues generated from both the green power and spot markets. Consequently, a cost-benefit contribution index system is developed to quantify the contribution of energy storage in the wind-solar-storage hybrid power plant. The revenue sharing model based on the minimum cost-remaining savings (MCRS) method can significantly increase overall revenue for renewable energy plants by reducing deviation penalties. It also enhances the operating revenue of energy storage power stations by considering the contributions of both energy storage and renewable energy plant in the green power market. The superiority of the proposed cooperation revenue sharing model for profitability enhancement of energy storage is validated through comparative case studies. [ABSTRACT FROM AUTHOR]

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility. [ABSTRACT FROM AUTHOR]

In a highly competitive electricity spot market, virtual power plants (VPPs) that aggregate dispersed resources face various uncertainties during market transactions. These uncertainties directly impact the economic benefits of VPPs. To address the uncertainties in the economic optimization of VPPs, scenario analysis is employed to transform the uncertainties of wind turbines (WTs), photovoltaic (PV) system outputs, and electricity prices into deterministic problems. The objective is to maximize the VPP's profits in day-ahead and intra-day markets (real-time balancing market) by constructing an economic optimization decision model based on two-stage stochastic programming. Gas turbines and electric vehicles (EVs) are scheduled and traded in the day-ahead market, while flexible energy storage systems (ESS) are deployed in the real-time balancing market. Based on simulation analysis, under the uncertainty of WTs and PV system outputs, as well as electricity prices, the proposed model demonstrates that orderly charging of EVs in the day-ahead stage can increase the revenue of the VPP by 6.1%. Additionally, since the ESS can adjust the deviations in day-ahead bid output during the intra-day stage, the day-ahead bidding strategy becomes more proactive, resulting in an additional 3.1% increase in the VPP revenue. Overall, this model can enhance the total revenue of the VPP by 9.2%. [ABSTRACT FROM AUTHOR]

The emerging threat of climate change underscores the necessity for increased investments in renewable energy sources and hydrogen energy to enhance the transition towards a more sustainable and resilient future. This work presents a simple Linear Programming (LP) mathematical framework to ensure the optimal portfolio management and participation of an aggregator owning a Polymer Electrolyte Membrane (PEM) electrolyzer and renewable energy assets in the electricity, hydrogen and green certificates markets. The objective aims to maximize the aggregator's overall profits by optimally managing green certificates issuance and diminishing the curtailment of the renewable energy production. The proposed approach is applied to an illustrative case study, while additionally a number of scenarios are considered to account for fluctuations in electricity price and wind power production. Furthermore, the impact of expanding electrolyzer's power capacity from the aggregator's side is also investigated. The results obtained illustrate the impact of the above-mentioned scenarios on the optimal operation and profitability of the aggregator. • Optimal scheduling of a RES-electrolyser aggregator in energy markets. • A novel concept for hourly management of available green certificates. • Wind power generation uncertainty greatly affects aggregator's scheduling decisions. • Aggregator optimally manages green certificates according to hourly prices. • Electrolyser power capacity expansion enhances aggregator's profitability. [ABSTRACT FROM AUTHOR]

The hybrid AC/DC grid, based on a significant share of renewable energy sources, is gradually becoming an essential aspect of the modern energy system. The integration of intermittent renewable generators into contemporary energy systems is accompanied by the decommissioning of power plants containing synchronous generators. Consequently, this leads to a reduction in system inertia and an increase in the risk of stability disruption. The abrupt disconnection of the primary generator or power line can result in an unanticipated mismatch between power generation and consumption. This discrepancy can trigger substantial and swiftly evolving alterations in power distribution, angular speed, load flow, and the frequency of generators. The risks of an energy system collapse can be mitigated through automation, enabling rapid adjustments to generation and load capacities, as well as power flows, in the electrical network. This article justifies the utilisation of a power control method for high-voltage power line interconnections. The technology of hydro storage power plants and measurements of voltage phasors are employed. The potential for easing power flow restrictions and realising substantial economic benefits is supported by the results obtained using simplified dynamic model of the Baltic power system and Nord Pool electricity market model. [ABSTRACT FROM AUTHOR]

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We study the impact of transition scenario uncertainty, namely that of future carbon price and electricity demand, on the pace of decarbonization of the electricity industry. To this end, we develop a theory of optimal stopping mean-field games with non-Markovian common noise and partial observation. For mathematical tractability, the theory is formulated in discrete time and with common noise restricted to a finite probability space. We prove the existence of Nash equilibria for this game using the linear programming approach. We then apply the general theory to build a discrete time model for the long-term dynamics of the electricity market subject to common random shocks affecting the carbon price and the electricity demand. We consider two classes of agents: conventional producers and renewable producers. The former choose an optimal moment to exit the market and the latter choose an optimal moment to enter the market by investing into renewable generation. The agents interact through the market price determined by a merit order mechanism with an exogenous stochastic demand. We illustrate our model by an example inspired by the UK electricity market, and show that scenario uncertainty leads to significant changes in the speed of replacement of conventional generators by renewable production. [ABSTRACT FROM AUTHOR]

This study addresses inherent challenges stemming from uncertainty associated with the integration of wind energy into the electricity market. A novel approach is proposed to leverage the capabilities of dynamic transformers to optimize the utilization of uncertain wind power output, thereby enhancing financial investment efficiency for wind power stakeholders. The flexible combination of wind turbines (WTB), transmission transformers (TTS), and Energy Storage Systems (ESS) can actively reserve or provision electricity. Electricity generation control is based on optimal analysis results using linear integer programming algorithms that consider temperature fluctuations, lifespan of transformers, and electricity market prices. Maximizing the dynamic transformer's efficiency as proposed and optimizing revenue and costs from the fluctuating wind power output significantly improves financial performance metrics when investing in wind farm projects. Financial figures highlighted in the paper emphasize notable benefits, particularly for wind farm expansion projects. The potential return on investment ratio is expected to increase up to 5.64 times compared to conventional wind farm investment scenarios, with an improvement to increase from 4.4% to 24.8%. [ABSTRACT FROM AUTHOR]

Currently, both regulated and deregulated power trading exist in China’s power system, which has caused imbalanced funds in the electricity market. In this paper, a simulation analysis of the electricity market with wind energy resources is conducted, and the calculation methods of unbalanced funds are investigated systematically. In detail, the calculation formulas of unbalanced funds are illustrated based on their definition, and a two-track electricity market clearing model is established. Firstly, the concept of the dual-track system is explained, and the specific calculation formulas of various types of unbalanced funds are provided. Next, considering the renewable energy consumption, the market clearing model based on DC power flow is constructed and solved; by combining fitting methods of mid- and long-term curves, the unbalanced funds are calculated based on clearing results and formulas. [ABSTRACT FROM AUTHOR]

In order to improve the operating benefits of the distribution network and reduce the energy consumption costs of small-micro-industrial parks, an electricity market clearing considering small-micro-industrial parks is proposed based on the Stackelberg game. First, an optimal operating model of multiple stakeholders is established for integrated energy suppliers, the electricity market, and small-micro-industrial parks. In this model, an optimal electricity supply model for integrated energy suppliers is established with the goal of maximizing the operating benefits. A market clearing optimization model is established for the electricity market with the goal of maximizing the social surplus profit. In addition, an energy utilization optimization model is established for the small-micro-industrial parks with the goal of minimizing the energy procurement costs. Second, with the electricity market as the leader, the integrated energy suppliers and the small-micro-industrial parks as the followers, a leader-follower game strategy is proposed based on the Stackelberg game theory to achieve the maximizing benefits for multiple stakeholders. Finally, the simulation indicates that the proposed strategy can find the best profit point during the game process and achieve a balance between supply and demand. [ABSTRACT FROM AUTHOR]

The concept of wholesale energy products (WEP) bears both complexity and synthetic nature of a new phenomenon. The complexity partly stems from the historically inherited specialization of technologies, which can now be combined into hybrid energy plants or energy hubs. WEP complexity causes a few specialized approaches and carve-outs with respect of every constituent commodity and service, however, it can be mitigated by policies promoting the universality of energy plants for rapid response to the changes in supply and demand. Physics of different substances that constitute WEP, such as natural gas, LNG, hydrogen, electricity, and their production, transportation, and storage infrastructure will remain a factor of distinction for the contractual and regulatory approach anytime soon. However, the impact of these differences has been and will be decreasing with hybridization of energy plants and popularization of the energy hubs that operate on different wholesale markets and, potentially, retail markets, too. Introduction of WEP effectively assumed that an energy hub becomes a new normal of energy flexibility on the market, of course, subject to further technology development, a high efficiency in converting power electrons to other forms of energy (methane and hydrogen molecules, for example) and vice versa. When (and if) such new normality happens, a further potential for uniform market rules is being realized - as was demonstrated in 2024 by expansion of the wholesale energy product definition in REMIT II. While LNG and hydrogen as well as their infrastructure are relatively new additions with some specifics to be ironed out, WEP can also be expected to expand further. The concept of a wholesale energy product had since 2011 led to the merger of both physical and financial instruments, so that many of them, especially physical products are subject to a double supervision; new contract forms and new data exchange standards had to be developed to optimize investments. [ABSTRACT FROM AUTHOR]

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This paper proposes a stochastic strategic bidding approach for a multi-energy microgrid (MEMG) to optimize its participation across electricity, thermal energy, and hydrogen markets. A MEMG powered entirely by renewable energy and integrating these three energy forms is designed using advanced energy conversion and storage technologies. A bi-level model is developed: in the upper level, the MEMG’s bidding strategies are optimized to maximize profits under operational constraints and market demands; in the lower level, detailed pricing mechanisms for each energy market are modeled, incorporating physical constraints and market competition. To address uncertainties in renewable energy generation, a chance-constrained approach is employed to mitigate potential market penalties. Moreover, a novel cost estimation method enables the MEMG to effectively price energy during trading. The bi-level problem is transformed into a tractable mixed-integer linear programming (MILP) problem using the Karush–Kuhn–Tucker conditions and linearization techniques. Numerical results show that the MEMG efficiently participates in multiple energy markets, reducing renewable energy curtailment and adjusting its trading strategies based on market conditions, thereby improving overall economic benefits.

This study provides a thorough analysis of unit commitment (UC) formulations in the context of low-inertia power systems, which are increasingly prevalent as the transition to Renewable Energy Sources (RES) gain momentum. Given the move towards more sustainable energy models, incorporating RES presents unique challenges in maintaining grid stability due to their inherent lack of synchronous inertia. This review examined the intricacies of modelling energy market designs and UC strategies to tackle these challenges. The study explores the expansion of frequency response services, emphasizing the increasing dependence on ancillary services, particularly in markets with a substantial presence of RES, attention is focused on the unique operating challenges in regions like Sub-Saharan Africa, where dispersed grid networks and generation inadequacies compound the problem. The paper delves into different methodologies and modelling approaches used in frequency-constrained optimization, shedding light on the strengths and weaknesses of current practices. A crucial outcome of our study is that the use of uniform frequency response models to deduce post-fault frequency parameters will not give adequate results in sparsely connected power system networks. Also, studies primarily focus on generation adequacy and the reliability criterion of the largest generator outage, overlooking the potential impact of network constraints and congestions in their formulation. Our analysis provides valuable insights into creating market frameworks that optimize energy and ancillary services, ensuring a stable and efficient grid operation in a power system dominated by RES. Based on our findings, it is essential to establish electricity market frameworks that can effectively maximize the utilization of energy resources and address the specific regional challenges associated with frequency regulation services in the future.

With the increasing share of wind power in the energy sector, many countries start to cut back supporting policies for wind power and shift towards market-oriented schemes, challenging the profitability of wind farms. Energy storage offers a flexible solution to enhance their profitability. This work explores different wind-related storage investment modes, including 1) direct ownership, 2) cooperative, and 3) competitive modes in a market-based environment. For the direct ownership mode, a bilevel single-leader-single–follower Stackelberg game model is proposed, where wind farms invest in and operate storage facilities strategically to maximize their profits in the upper level, while the lower-level problem represents the system operator’ s market-clearing process. A cooperative game framework is presented for the cooperative mode, that wind farms and storage investors agree on a profit allocation rule, i.e., Shapley value or Nucleolus to collaborate in investing and bidding as a coalition. The competitive mode is interpreted as a multi-leader-single-follower Stackelberg game, describing an independent investor investing in and operating storage facilities in competition with wind farms. Case studies conducted on a 6-bus and the IEEE 30-bus test systems demonstrate that storage facilities directly invested in by wind farms are the best option for maximizing their profits, resulting in up to an 8.7% increase. The cooperative option provides a suboptimal increase of up to 3.1%, diversifying the costs and risks associated with storage investments. In contrast, the competitive mode can diminish wind farms’ profitability, with up to a 30.6% decrease in profits.

With the increasing integration of smart renewable energy systems and power electronic converters, electricity market price prediction is particularly important. It is not only crucial for the interests of power suppliers and market regulators but also plays a key role in ensuring the reliable and flexible operation of the power system, particularly during extreme weather events or abnormal conditions. This study develops a hybrid time series forecasting model that combines Prophet and Transformer, which takes advantage of deep learning to provide a new solution for electricity market price forecasting. By introducing the Stacking optimization strategy, this study improves the accuracy and stability of electricity market price sequence prediction. In addition, the study tries to integrate traditional time series forecasting methods (such as the Prophet model) with deep learning models (such as the Transformer model), aiming to make full use of their respective advantages to achieve more accurate and stable predictions. Through experimental evaluation on four electricity market data sets, this study finds that the hybrid forecast model exhibits significant performance improvements in enhancing the accuracy and stability of electricity market price predictions. This method not only provides a more accurate tool for power market price prediction, but also provides solid technical support for the efficient operation and sustainable development of smart renewable energy systems. Experimental results also show that the combination of deep learning models with traditional time series methods and the introduction of Stacking strategies is crucial to improving the performance of power market price prediction, and also helps us better understand and design smart renewable energy systems, price and energy management strategies, thereby providing an effective method for achieving efficient and reliable power and energy transmission.

Massive and diversified distributed energy resources (DERs) have great potential for improving the flexibility of power system operations. However, given the wide distribution, small capacity, and variable ownership subject characteristics of DERs, virtual power plants (VPPs) are suitable for aggregating the flexible DERs for participation in the wholesale energy market. Extant studies have focused on obtaining bidding strategies in multiple electricity markets. However, there are shortcomings in the relevant market mechanisms and temporal relationships for participating in energy and ancillary service markets. This work proposes a joint market temporal rolling bidding strategy for VPPs in the wholesale market based on real unified electricity market mechanisms in the southern region of China. Accordingly, the proposed method enhances the market-oriented operational profits of VPPs.