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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.

This paper handles the problem of optimal charging control for electric vehicle (EV) aggregators in energy and ancillary service markets under uncertainties. We propose a robust optimal coordinated charging (OCC) model that formulates a robust linear programming (RLP) for the EV aggregator formed on unidirectional vehicle-to-grid (V2G) and coordinates the provision of regulation and spinning reserves. Our model considers many uncertainties in the electricity markets, like ancillary service prices and the placement signals, as well as accurate parameters associated with EV behavior, like EV availability, time of trips, and trip duration. The objective of the optimization is to maximize the aggregator's income from V2G by contributing to the ancillary services markets. The proposed robust OCC model, a robust linear problem (RLP) model, is simulated using the CPLEX solver in GAMS software. We compare our model with the existing deterministic models and show that our model increases the aggregator's revenues and then reduces the deviation between predicted and realized revenues. We propose a new and effective method for EV aggregators to bid into energy and ancillary service markets under uncertainties. Our model shifts the charging time of EVs from peak to off-peak hours, resulting in an improved final state of charge.

Recently, demand response (DR) programs are being promoted in the power market to strengthen the role played by the load side in the power market and to increase the flexibility of loaders to participate in the power market. Data centers are uniquely positioned to participate as large-scale power users in the power market operation process. However, the issue of load uncertainty risk is not adequately considered in recent DR programs for data center participation in the power market. Regarding this issue, we propose a framework for facilitating bids for data center participation in DR programs in the power market. In the proposed framework, the data association Dirichlet process mixture model (DA-DPMM) is introduced to characterize the load uncertainty, and the association of load data is investigated to obtain higher prediction accuracy. To further reduce the uncertainty risk, we propose a two-stage market cleaning model based on the conditional value at risk (CVaR). We also analyze the CVaR, the social welfare derived from the Gaussian process model (GM), the Dirichlet process mixture model (DPMM), and the DA-DPMM. The results demonstrate the superiority of the DA-DPMM approach in a two-stage market cleaning model based on CVaR.

This paper addresses the conundrum of optimizing electricity consumption patterns in response to fluctuations in demand and price, a task managed by both load aggregators and the distribution system operator (DSO). The conventional approaches in the literature to integrate demand response (DR) into optimal power flow (OPF) problems typically overlook the price responsiveness of consumers or simplify power flow equations to account for price-elastic demand.In this paper, we strive to close this gap by introducing a bi-level primal-dual optimization framework that incorporates aggregators' objectives into the market-clearing process while preserving the precision of the OPF equations. At the upper level, the model seeks to minimize both the total payment and peak load. The lower level, structured as a second-order conic (SOC) problem, aims to reduce overall generation costs subject to constraints of the second-order conic (SOC) branch flow model (BFM). The two levels interact through the optimal demand response and distribution locational marginal prices (DLMPs). The principles of convex duality principles together with the strong duality constraint are leveraged to transform the market-clearing problem into a single primal-dual problem. We also circumvent the non-linearity that stems from the DR payment term by incorporating discretizing loads and the big-M method, thus converting the problem into a mixed-integer SOC (MI-SOC) formulation. The merits of the proposed MI-SOC framework are validated through case studies conducted on the IEEE 33-bus test system, showcasing its potential to enforce price-elasticity demand response in distribution system's electricity markets.

Cross-border electricity trade can promote the sustainable development of regional multi-country economies by alleviating the uneven distribution of global energy resources. Hence, designing a reasonable and effective mechanism contributes toward the development of cross-border electricity market. This study established a non-cooperative game model for cross-border electricity trading. Central to our approach is the analysis of multi-participants trading modes influenced by cross-border trading centers. The model distinctively incorporates trading rules and service fees, providing a realistic simulation framework. The results reveal a direct correlation between strategic scope and the dependency of trading strategies on electricity pricing set by trading centers. This research advances the theoretical understanding of game theory application in electricity markets and offers practical insights for policymakers and market operators, particularly in strategizing for price setting and managing cross-border transactions.

This research paper delves into the central mechanisms implemented in Spain to foster the adoption of clean energy projects. The study scrutinizes various mechanisms established from 2005 onwards, evaluating their efficacy in achieving the desired outcome of supporting clean energy project implementation. The aim is to comprehensively assess the associated costs, effectiveness, and attainment of renewable energy objectives stemming from implementing diverse support schemes. This paper explores the effectiveness of feed-in tariffs and feed-in premium mechanisms in promoting the integration of new renewable energy generation into the power system. However, the Spanish experience suggests that exhaustive planning control is imperative to prevent excessive support costs that could jeopardize the entire market and cause a significant rise in final energy costs. The research studies the support schemes and associated market distortion and presents the roadmap from regulated tariffs to a “total market” for wind and solar energy. The findings demonstrate that, under proper supervision and planning, a transitional retribution scheme effectively promotes renewable energy plant integration. Finally, the analysis examines the need to regulate the excess benefits that non-emitting power sources receive in a marginal price market, as this can lead to market imbalances and a substantial increase in final costs. These plans serve as a crucial tool in achieving the decarbonization objectives of the European Power System.

Gathering knowledge of supply curves in electricity markets is critical to both energy producers and regulators. Indeed, power producers strategically plan their generation of electricity considering various scenarios to maximize profit, leveraging the characteristics of these curves. For their part, regulators need to forecast the supply curves to monitor the market’s performance and identify market distortions. However, the prevailing approaches in the technical literature for analyzing, clustering, and predicting these curves are based on structural assumptions that electricity supply curves do not satisfy in practice, namely, boundedness and smoothness. Furthermore, any attempt to satisfactorily cluster the supply curves observed in a market must take into account the market’s specific features.Against this background, this article introduces a hierarchical clustering method based on a novel weighted-distance that is specially tailored to non-bounded and non-smooth supply curves and embeds information on the price distribution of offers, thus overcoming the drawbacks of conventional clustering techniques. Once the clusters have been obtained, a supervised classification procedure is used to characterize them as a function of relevant market variables. Additionally, the proposed distance is used in a learning procedure by which explanatory information is exploited to forecast the supply curves in a day-ahead electricity market. This procedure combines the idea of nearest neighbors with a machine-learning method. The prediction performance of our proposal is extensively evaluated and compared against two nearest-neighbor benchmarks and existing competing methods. To this end, supply curves from the markets of Spain, Pennsylvania-New Jersey-Maryland (PJM), and West Australia are considered.

Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.

Energy communities have increasingly become functional actors in European energy markets. Recently, different designs of the renewable energy trade among the stakeholders of an energy community project have gained traction. However, the connection between those designs and the (re)distribution of the social costs and benefits from the project has remained under-represented. There is a need to investigate the connection in light of the emerging energy regulation innovations. In this context, We conducted a social cost-benefit analysis (SCBA) to evaluate the socioeconomic impact of an ongoing solar park project owned by a local energy community in the Netherlands. The findings of the SCBA reveal a negative socioeconomic impact of the project in its current form. Therefore, we suggest that it is necessary to change the business model underlying the project and propose a recommendation on the existing regulation. To trigger a positive impact, we propose that the energy community is required to shift its revenue stream from trading electricity with grid operators to trading electricity directly with participating residents.

At present, an increasing number of countries and regions, including China, are adopting electricity and carbon markets to optimize the resource allocation of the electricity power industry. To mitigate the impacts of these complex market conditions, more and more power generation enterprises adopt hybrid production mode, generating electricity from thermal and new energy units simultaneously. This research introduces the decision process of hybrid power generation enterprises under the abovementioned trading conditions, and provide the optimal production decisions of enterprises with different market positions. Besides, the consequences of collusion behaviors among power generation enterprises and governmental policy changes are also measured based on the description to the equilibrium state of electricity and carbon markets. Numerical experiment results confirm the correctness of the production decision model and reveal the following facts: (1) Carbon market effectively reduces carbon emissions and raises the new energy power generation share, but it cuts the electricity supply and drives up the electricity clearing price. (2) The combined effects of electricity and carbon markets are able to mitigate the impacts of policy changes on market equilibrium. Power generation enterprises first internally cushion these impacts by adjusting the generation structure, and then absorb the residual impacts by adjusting the total power generation. (3) The collusion behaviors among power generation enterprises can destroy the electricity and carbon markets largely. They increase the CO2 emissions by 187.94%, cutting the electricity supply by 25.30%, and drive up the electricity clearing price by 1.37%.

The high operating costs of Internet Data Centers (IDC) are a major challenge for their owners worldwide. Therefore, more attention has recently been paid to the energy and cost management of IDCs. This paper investigates the optimal operational strategy for minimizing the electricity costs of a group of globally distributed IDCs in different locations under various day-ahead electricity markets, and each is equipped with a high-performance energy storage system. For this goal, optimal workload dispatching and optimal energy management of the storage units of all IDCs are simultaneously perused by the proposed problem. The system is modeled regarding power balancing constraints, battery costs, and quality of service (QoS). For more practical results, a penalty function is also considered when QoS constraints are not perfectly met, and the impact of the batteries’ depth of discharge on the cost of energy storage is also modeled. Moreover, the cross-correlations between the traffic of IDCs are also considered by the multidimensional copula function. The proposed energy cost optimization is linearized for increasing the accuracy of convergence. The results show that not only the power consumption pattern of the IDCs is significantly improved, but also the cost of power consumption is reduced by 34%. The results also prove the positive effect of battery discharge on workload dispatch and represent a compromise between battery costs and electricity cost savings.

Timing of generation is becoming more and more valuable. This creates greater potential tension between environmental and power system objectives since both systems require their own flow patterns. Identifying win–win outcomes in this context requires being able to discuss the value of flexibility across stakeholder groups. This research proposes a two-stage optimization method to understand hydropower flexibility to meet both environmental and power system requirements. The tool simulates the two-settlement market process in the U.S. by maximizing revenues from both the day-ahead and real-time markets, subject to plant operational limits, regulatory flow and ramping requirements, and uncertainties associated with water availability and market prices. The model is formulated as linear programming problems and solved using IBM ILOG CPLEX optimizer. By examining a range of flow requirements, ramping constraints, and storage capacities, the proposed tool shows how to make more informed decisions to weigh the cost of specific flow requirements in the context of the overall license requirements. Results from the case study show that revenue is more sensitive to the ramping constraints than the minimum flow constraints. We also demonstrate that removing flow constraints in a dry month increases monthly revenue by up to 118%, as opposed to only 1% in a wet month. In addition, our results suggest that using a learning-based water flow forecast results in an increase of monthly revenue up to 6.4% compared with persistence forecast.

In recent years, the proportion of solar power generation in Japan's power generation facilities has been increasing. The amount of solar power generation affects the JEPX (Japan Electric Power Exchange) spot prices. When the amount of solar power generation is excessive during the low demand period, the lowest prices sometimes appear in the spot market. On the other hand the existing forecasting method cannot forecast the lowest prices; hence the forecasting error of the existing method is large. In this study, the authors focused on the appearance of the lowest prices and analyzed the conditions under which the lowest prices are likely to appear. As a result, the authors identified the conditions where the lowest price is likely to appear by comparing with the season and past forecasted solar power generation. Then, the JEPX spot prices forecasting system was developed considering the appearance of the lowest prices. The system combined these conditions, and the authors developed the JEPX spot prices forecasting system. This system can accurately forecast the appearance of the lowest prices and spot prices.

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.

With the demand for energy sustainability and the development of the low-carbon power system, distributed energy, energy storage, demand response, and other diverse market participants will participate in the electricity market, which brings new changes to the transaction organization process of the electricity market. This paper first introduces the changes in the electricity market under the participation of diverse market participants from two aspects of market transactions and market power. Diverse participants can provide a variety of auxiliary services in the electricity market while they have potential market power in the process. Secondly, the current supervision measures used in the foreign electricity market in the order of ex-ante, interim, and ex-post are reviewed and four common supervision indexes are introduced. Finally, the market power and the supervision necessity of the electricity market under the participation of energy storage are analyzed by case studies. As shown by the simulation results, energy storage with excellent regulating performance has market power in providing frequency regulation services, but the conventional supervision indexes fail to detect the market power of energy storage effectively. The purpose of this paper is to illustrate the necessity of the market supervision method for adapting to the changes under the participation of diverse participants.

China is pushing ahead with the development of the electricity market based on renewable energies. Electricity futures and options are an important trading instrument in the electricity market, and margins in the electricity market are often used to measure the value at risk of derivatives such as options on electricity futures, which are usually calculated based on the SPAN system. However, traditional SPAN systems do not take into account the integrity of electricity (IoE) derivatives, which would affect the risk valuing and capital usage efficiency. This paper proposed an improved margin calculation method that considers the IoE and gave an example for explanation. Compared with the previous calculating method, the method can effectively control the sharp fluctuations in electricity prices when renewable energy sources are traded in the electricity market, and effectively control the default risk in the market.

With the increasing renewable energy and expanding trans-regional transmission, higher requirements are placed on the outage maintenance scheduling for high-voltage power transmission networks. In this paper, a bi-level multi-area joint maintenance scheduling optimization model is established for high-voltage transmission lines taking into account the consumption of renewable energy under two-level market environment. This model is able to achieve the following in terms of the maintenance scheduling: (i) It adapts to the optimization objectives and modes of different electricity spot markets between and within areas; (ii) The goals of maintenance willingness and consumption of renewable energy are balanced; (iii) By using the Kuhn–Tucker method, the bi-level model is transformed into a single-level mixed integer programming problem with complementarity constraints for the purpose of solving. The method proposed in this paper is verified by numerical examples, which reveal how the proposed model can improve the capability of multi-area power system to accommodate renewable energy and better arrange the maintenance resources.

Energy, greenhouse gas emissions, or water taxes, are present in many power systems as available instruments to implement energy, environmental or climate policies. The goal of these taxes is to change producers’ optimal behavior and hence achieve a cleaner operation. However, these changes in operation are not straightforward to simulate when producers are price makers, or when assets are owned jointly by different agents. State-of-the-art models are unable to consider differentiated income taxes per technology or geographical region, in the case of price makers. In this paper, we present a novel formulation that models the individual market income of each unit using the binary-expansion technique to address the case of a price-maker agent. Unlike existing state-of-the-art formulations, our approach successfully accounts for differentiated income taxes per technology or geographical region and accurate market revenues of shared generators. The proposed model enables evaluating the rational behavior of a generation company confronting a complex yet realistic decision problem, under different types of taxes related to decarbonization or resource conservation policies. The case study incorporates the impact of the installation of carbon sequestration and storage equipment in a fleet of gas-fired power plants, yielding satisfactory numerical results.

Against the backdrop of the gradual advancement of China's electricity market reform, the number of Power Trading Companies in China has been increasing year by year, and as of October 2022, the number has reached more than 10,000. As an important hub connecting the electricity market and users, electricity retailers face double risks from downstream user load fluctuations and electricity market price fluctuations. Therefore, a reasonable power purchase and sale strategy is very important for an electricity retailer. In this study, a block bidding mechanism is adopted to optimize the clearing of the medium-to long-term market and a DA-RBF neural network is established for spot electricity price forecasting model based on numerical feature similarity to improve the accuracy of electricity price forecasting. Furthermore, the model considers the differences in user demand responses and investigates the optimal power purchase and sale strategy, guided by differentiated time-of-use electricity pricing. The case study analysis demonstrated that the proposed power purchase and sale optimization strategy yields favorable results, improving profitability and enhancing the stability of the power system.

Globally, the power sector must undergo a profound transition to achieve the decarbonization development targets. Various roadmaps are implemented, but only from a macro perspective, lacking the consideration of the electricity market rules. In this paper, we develop and present a market-driven generation and transmission expansion planning (MGTEP) model considering the effectiveness of the electricity market. Specifically, generation and transmission companies incorporate hourly market trading and annual capacity investment into strategic decisions to maximize their profits, with the supply function equilibrium model to analyze bidding behaviors. An equivalent quadratic programming formulation is deployed to solve the trilevel MGTEP model. Meanwhile, the MGTEP model is coupled with decarbonization policies to support the state and federal government in assessing energy transition strategies. We implement the MGTEP model with carbon emission allowance and carbon tax policies for the southern China electricity market to achieve carbon peaking by 2030. Carbon emission allowance adopts an intensity-based cap based on generation companies' historical output. The case study results show that 50 % carbon emission allowance or 400 CNY/t carbon tax is required but with several drawbacks, including unsatisfactory decarbonization effect, excessive economic sacrifice, etc. Finally, the case study is extended to dual-track policies with different combinations of policies. An optimal combination is 70 % carbon emission allowance and 160 CNY/t carbon tax. In this case, the power sector's carbon dioxide emissions and electricity prices in the southern China electricity market would increase to 554.6 Mt and 864.34 CNY/MWh in 2030, respectively, along with a carbon price of 850 CNY/t.

The stochastic output of wind power will lead to the penalty of bidding deviation in the spot market and bring bidding risk, restricting the participation of wind power in market competition. The combined bidding strategy for multiple power generation stakeholders can help alleviate the output uncertainty of wind power, reducing the bidding deviation penalty. This paper aims at the modeling method for wind-thermal combined bidding in the spot power market. Conditional value at risk (CVaR) is used to describe the bidding risk of the combined bidding. To better reflect the bidding risk, the bilateral tail information of the upper and lower deviations is modeled according to the revenue function. The uncertainties of wind power output and the clearing price in the day-ahead market are considered, and the risk decision-making model for wind-thermal combined bidding is established. The model is solved by the chaotic particle swarm optimization algorithm with constraint handling (EDFC-PSO). The results show that the proposed modeling method can describe the risk indicators in detail, providing a better reference for market bidding. Also, the combination of the wind farm and the thermal power unit can reduce the risk of wind power in bidding and improve its profitability in the day-ahead market. Besides, the risk speculation of the wind farm can be found when facing the inevitable increased risks. It means the behaviors of the market participants can be better explained using the proposed method in this paper.

With the development of smart grids, optimizing the performance of response programs in the distribution system in operation and development, held special importance. This paper proposes an optimized method for demand response management in smart grids using real-time pricing. The method involves defining an acceptable consumption pattern and energy price through consumer-utility interaction. For more evaluation, six scenarios are defined that considered different states of energy pricing with and without the Plug-In Electric Vehicles (PHEVs) The first step establishes the demand response with real-time pricing, determining hourly load and energy prices. Price elasticity of demand is employed to gauge consumer load response to energy prices. The second step utilizes the Geometric Mean Optimizer (GMO) to optimize Demand Response Programs (DRPs) for consumers with smart programmable meters. The proposed method is tested on the IEEE-69 bus distribution network under various scenarios, demonstrating its effectiveness under 48 h. Additionally, the study investigates the impact of PHEVs and their controlled charging using the integrated three-tariff method and real-time electricity pricing. The results of the GMO algorithm are also compared with other methods to validate its advantages. The results indicate enhanced voltage profiles and reduced reactive power consumption during peak hours about 4.5% and 23%, respectively. in addition, the load factor by applying the proposed method is increased by about 18%. Moreover, the proposed method by combining the three-tariff method enables the transformer to supply approximately 60% of EVs without overloading, which is 40% more than that case without the three-tariff method.

Wind energy has been increasingly adopted to mitigate climate change. However, the variability of wind energy causes wind curtailment, resulting in considerable economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) as onsite backup sources. Yet, this auxiliary role may significantly weaken the economic potential of BESS in energy trading. Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding, but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation. We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets. We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy, with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25% and more wind curtailment reduction by 2.3 times. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately. Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains. The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

Resulting from the mismatch of constraints between unit commitment and economic dispatch, constraint relaxation is commonly utilized in market-clearing models to remain a feasible solution. If the constraint relaxation is active, price spikes may incur. Previous work has proposed a pricing method to handle the issue, which takes the prices as the decision variables and formulates the relationship of incentive compatibility. However, the previous work can be enhanced in two parts: (1) the multi-period influence is not incorporated, which is not applicable for the day-ahead market; (2) the market surplus of the previous method is irrationally large. To resolve the issues, this paper proposes a multi-interval pricing method based on bi-level optimization. The market surplus constraint is included in the proposed pricing model and the multi-interval incentive-compatibility constraint of each generator is formulated. To solve the pricing model reliably, the bi-level optimization model is transferred into a mixed-integer linear programming problem based on KKT conditions. The effectiveness of the proposed method is verified in the IEEE 30-bus system.

After 1980s, liberalization in energy industry accelerated. At that times, private sector started to show more tendency to electricity thanks to regulations and policies made by governments so electricity markets emerged and spread very rapidly, which created market risk that needs to be managed carefully along. Decision makers in electricity generation industry had to be faced this market risks besides other operational risks. They had to review policies regarding to risk management and they noticed that determining the right bidding strategy for electricity market and bilateral contract market was crucial. In this paper, Mean–variance, Semi-variance, and Down-side risk methods, which are common in portfolio optimization of financial literature are used to manage risk and to optimize electricity market bidding strategies and decision policy for an electricity generation company. Apart from the other limited studies, performances of optimal portfolio solutions are measured and further more improved with the help of Sharpe and Treynor ratios for electricity market. It is seen that direct use of portfolio management tools in electricity markets can cause sub-optimal solutions, so risk aversion constant of utility functions should be adapted. This study shows that optimal bidding strategies for electricity generators can be improved with the help of Sharpe and Treynor ratios. In order to demonstrate the results, two consecutive years of Turkish Day-ahead Market data are used in an empirical case study.

Electric vehicles (EVs) combined with low-carbon generator sets can significantly reduce CO2 emissions in the transportation sector. EVs can provide flexible auxiliary services for the grid through proper power dispatching, which benefits both EVs and the grid. This paper discusses the optimal charging and discharging plan for EVs for the purpose of a distribution system operator (DSO) while obtaining the optimal day-ahead bidding strategy for DSO in the spot market. Considering the different charging demands of EV users, two contract models of vehicle-to-grid (V2G) and smart charging services are introduced. Then, K-means clustering is adopted to manage the spatiotemporal uncertainties of EVs, in order to maximize the expected marginal revenue of DSO. Then the optimization model is proposed by considering the real conditions campus grid in Shenzhen and the electricity market in Guangdong province. The result shows that the DSO could obtain the marginal revenue not only by the optimal day-ahead bidding strategy but also by scheduling EVs’ charging and discharging. In addition, EV users could reduce the charging cost by charging their EVs during low electricity price periods and get extra revenue by exporting power to the utility grid during high price periods.

Under the background that the spot market has not yet been fully established, the risk of coal price fluctuation lacks an effective mechanism to transmit to users. During the period of high coal prices, coal-fired units suffered serious losses and the willingness to generate electricity declined extremely, which was not conducive to the stable growth of the national economy. To solve the above problems, after investigating domestic and foreign solutions, this paper conceived a designing idea for the index-based long-term electricity market contract suitable for the current electricity market. Based on the actual data from a province in China, the specific parameters of index-based contract were designed and the profit of units were analyzed. The results show that the profit fluctuations of the coal-fired units will be significantly reduced after the trading contract adopted. In the end, the key issues of trading contract and the impact on market entities were discussed.

This paper proposes a double-tier bidding(DTB) model on the generation side to introduce gas-fired units(GFU) into the market bidding by analyzing the electricity cost of the whole society and the generation cost of gas-fired units. With the introduction of a sub-process, the GFUs are incorporated into the main bidding process or the sub-process independently, and the bidding prices of each process are cleared separately. Based on the traditional market bidding algorithm, the DTB model can effectively solve the problem of increasing market price after units with higher generation costs join the market. Besides, transmission congestion and power shortages during peak hours caused by the drawback of market mechanism can be avoided. In addition, recessive electricity price subsidy from markets is visualized, realizing the non-price advantages of low-emission units. Thus, the promotion of the overall optimal social benefits and the implementation of the low-carbon development concept are realized. Moreover, the algorithm incorporates a transverse comparison method of generation costs to restrict the market speculation of power suppliers. Numerical example shows that this algorithm is effective and feasible, and can be adopted in other areas with similar market characteristics.

The carbon emission right is considered as an effective way to reduce carbon emissions in the power industry, especially under the global goal of carbon neutral. Considering that carbon emission cost will affect long-term profits, the bidding strategies of power generation companies in electricity market and carbon emission market need to be coordinated. Given this background, a bi-level bidding strategies optimization model is proposed for power generation companies, which considers the carbon emission right and aims to maximize their annual power generation benefits. The purchase of carbon emission right at the beginning of the year is optimized in the upper-level model with the goal of maximizing the expected annual income and the uncertainty of transaction volume in monthly bidding market considered. The remaining carbon quota, penalty electricity price, and uncertainty of the competitor’s quotation in monthly electricity market are considered in the lower-level model, based on which the bidding strategy for power generation company with the goal of maximizing the monthly expected income is proposed, and the power generation company’s electricity market bidding model and its clearing results are simulated. Simulation results shows that power generation companies’ purchases of carbon emission rights and their bidding strategies in the monthly market are affected by the carbon emission penalty price. The higher the penalty price is, the greater the quotation and the purchase of carbon emission rights are.

Blockchain technologies are considered one of the most disruptive innovations of the last decade, enabling secure decentralized trust-building. However, in recent years, with the rapid increase in the energy consumption of blockchain-based computations for cryptocurrency mining, there have been growing concerns about their sustainable operation in electric grids. This paper investigates the tri-factor impact of such large loads on carbon footprint, grid reliability, and electricity market price in the Texas grid. We release open-source high-resolution data to enable high-resolution modeling of influencing factors such as location and flexibility. We reveal that the per-megawatt-hour carbon footprint of cryptocurrency mining loads across locations can vary by as much as 50% of the crude system average estimate. We show that the flexibility of mining loads can significantly mitigate power shortages and market disruptions that can result from the deployment of mining loads. These findings suggest policymakers to facilitate the participation of large mining facilities in wholesale markets and require them to provide mandatory demand response.

This paper analyzes the operation of an energy hub on a community level with an integrated P2X facility and with access to energy markets. In our case, P2X allows converting power to hydrogen, heat, methane, or back to power. We consider the energy hub as a large prosumer who can be both a producer and consumer in the markets with the novelty that P2X technology is available. We investigate how such a P2X energy hub trades optimally in the electricity market and satisfies local energy demand under the assumption of a long-term strong climate scenario in year 2050. For numerical analysis, a case study of a mountain village in Switzerland is used. One of the main contributions of this paper is to quantify key conditions for profitable operations of such a P2X energy hub. In particular, the analysis includes impacts of influencing factors on profits and operational patterns in terms of different degrees of self-sufficiency and different availability of local renewable resources. Moreover, the access to real-time wholesale market electricity price signals and a future retail hydrogen market is assessed. The key factors for the successful operation of a P2X energy hub are identified to be sufficient local renewable resources and access to a retail market of hydrogen. The results also show that the P2X operation leads to an increased deployment of local renewables, especially in the case of low initial deployment; on the other hand, seasonal storage plays a subordinated role. Additionally, P2X lowers for the community the wholesale electricity market trading volumes.

Introducing P2P energy trading in the consumers’ local market makes the energy utilization rates of users improved significantly. However, the output uncertainty of distributed energy sources poses challenges to the normal conduct of the P2P transaction. The uncertainty sources have to pay for the reserve resources for the possible power gap. The difference between the actual output and the day ahead market planning needs to be charged to punish the uncertainty producers. Therefore, a novel pricing strategy (uncertainty marginal price, UMP) is proposed in this aper to charge the uncertainty producers and credit the reserve resources. Voltage sensitivity coefficients, power transfer distribution factors and loss sensitivity factors are introduced to linearize the power flow model. The whole problem can be formulated as a robust model and decomposed into two subproblems. CCG algorithm is applied to solve the two-stage robust model. This new pricing strategy is verified on the IEEE-33 and 69 bus systems. Numerical results indicate that the proposed price increases with the rise of uncertainty level. The reference points of the system robustness can be chosen as the inflection points of the operation cost curves under different uncertainty levels.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

Considering the impact of the restriction of the weight of the responsibility of consumption on the promotion of local renewable energy consumption and the cross-provincial transaction of renewable energy, this study examines the multi market clearing decision-making problem. Additionally, it proposes and constructs the intraprovincial and interprovincial bi-level market clearing decision-making model. The objective function of the upper model is to minimize the operation cost of the power market at the intraprovincial level considering the weight constraint of consumption responsibility, and the objective function of the lower model is to minimize the renewable energy purchase cost at the interprovincial level. In the two-level model, the two levels are coupled with each other, and the Karush–Kuhn–Tucker (KKT) condition of the lower level model is used to transform the two-level model into a single-level nonlinear model. The strong duality theory and Big M method are used to transform the nonlinear model into a linear model, and the Gurobi solver is used to solve the problem. The results show that the optimal interprovincial renewable energy purchasing power is achieved at the minimum market operational cost in the province. In addition, the effectiveness and rationality of the model are verified by the example analysis.

This paper provides a comprehensive review of the current state and planned developments in the electricity sectors across eight countries in South East Europe (SEE). It highlights positive examples as well as barriers to efficient market operation and regional market integration. Progress is evaluated in terms of the level of implementation of required legislation, and the efficacy of regulatory frameworks to facilitate market integration of renewables, emerging technologies, and active demand participation. The observations are based on exhaustive research into European Union (EU) and national legislation, as well as a questionnaire taken by industry experts from the corresponding Transmission System Operators offering first-hand insights into the electricity sectors in the region. The conducted investigation demonstrates that SEE countries are at different stages in market opening and compliance with EU energy policy but show unity in the aim of integrating into the EU internal electricity market. The harmonization of legislation and technical requirements is highlighted as a precondition to this goal as it would facilitate cross-border trade, increase efficiency through the shared use of resources, and incentivize infrastructure investments. The analyses show that there is a continuous progress in development of wholesale and balancing markets in the region, but problems with liquidity and incumbents impede the process. The progress towards renewable generation targets varies between the countries, however, the region is committed to electricity sector decarbonization and digitalization in the future. Finally, the paper presents potential directions for regional power sector developments in response to the challenges in electricity market design for a low-carbon future.

In this paper, we explored the interplay between the electricity market structure, methods of electricity trading and different stakeholder dynamics within the Nigerian Electricity Supply Industry (NESI) with a view to understanding how these interplays impact on various forms of interventions in the Nigerian electricity sector. We started off by exploring the market structure and electricity trading system within the Nigerian electricity sector and reviewed the various stakeholder groups within centralized and decentralized electricity systems in Nigeria's electricity sector by highlighting their core responsibilities and the dynamics at play in satisfying their interests. This study revealed that: (1) external stakeholder groups (such as donor agencies and multi-lateral organizations) exert more influence in Nigeria's electricity sector through financial interventions; (2) lack of coordination and engagement among various stakeholder groups pose a challenge to effective electricity infrastructure interventions that address the needs of people in society. The study concludes by highlighting the implications of these challenges and the need to address the rising complexities and uncertainties for better stakeholder involvement in addressing the salient challenges in the sector.

The rapid growth of intermittent renewable energy for electricity production brings a huge challenge to keep the balance between supply and demand in power systems, which further raises the need of flexibility. It has been well recognized that combined heat and power plants (CHPs) can play an important role in the provision of flexibility, due to their ability to switch the production between electricity and heat. However, there still lacks a comprehensive review about the flexibility provided by individual technologies involved in CHPs, which hinders the exploration of the capability of CHPs. In addition, new technologies and technology couplings have been proposed and adopted to further improve the flexibility in CHPs. This review aims to characterize the flexibility features of technologies that have been adopted or can be adopted by CHPs. Ramp rate, operation range, and power capacity are selected as key performance indicators for assessing technologies for providing flexibility services. Based on the collected result, suggestions are provided regarding the selection of technologies to enhance the flexibility provided by CHPs.