Search

Cross-regional electricity trading is critical to optimizing energy resource allocation and enhancing societal benefits. Reviewing theoretical research on key mechanisms of cross-regional electricity markets and drawing lessons from typical markets are of great significance for developing such markets. Firstly, key mechanisms of cross-regional electricity markets are outlined, including market basic structure, trading modes, market coordination mechanisms, and transmission pricing mechanisms. Secondly, the key mechanisms of typical cross-regional trading markets, including those in Europe, the United States, and China, are analyzed and compared. In addition, a summary analysis is conducted on the problems existing in further developing typical regional electricity markets towards cross-regional trading markets, along with corresponding solutions. Moreover, the key issues needing focus in different mechanism designs of cross-regional trading markets are summarized to provide theoretical foundations for other countries or regions.

The Virtual Power Plant (VPP) serves as the primary form of participation for distributed power generation resources in electricity markets. Throughout this market engagement, uncertainties stemming from renewable energy sources and market prices present significant revenue risks for VPPs. Addressing this challenge, the paper proposes a VPP bidding risk management model centered on dominance constraints. In contrast to conventional methods reliant on risk metrics like Conditional Value at Risk, the proposed model offers improved control over downside and tail risks while reducing optimization iterations to just one, thus substantially reducing computational cost associated with risk management. The study also contrasts the efficacy and computational costs of first-order and second-order dominance models. For instance, in a three-scenario benchmark, the first-order dominance model exhibits a 16.7% lower overall risk level compared to the second-order model. Similarly, in a nine-scenario benchmark, the computational time of the first-order model increases by 0.96% due to the introduction of binary variables, and this increase scales with the number of benchmark scenarios. The paper additionally incorporates mean deviation of profits and expected excess returns to develop a bi-objective optimization model. This model not only fulfills risk control requisites but also shapes return distributions to align more closely with VPP decision-makers’ profit preferences. The case study is based on a VPP comprising conventional thermal units, wind turbines, energy storage stations, and flexible loads.

Numerous short-term load forecasting models are available in the literature. However, the improvement in forecast accuracy using the combination models has yet to be analyzed on a daily rolling basis for a very long test period. In this paper, the characteristics of a combination of the Seasonal Autoregressive Integrated Moving Average (SARIMA) – a linear model and Radial Basis Function networks (RBFN) – a non-linear model have been studied in two different modeling frameworks, namely single series (SS) and variable segmented series (VSS). The hourly load data from the Ontario Electricity Market (OEM) and the Iberian Electricity Market (MIBEL) are used for the analysis. This dataset spans 12 years for OEM and one year for MIBEL. The impact on prediction accuracy by the size of training data and the combining individual forecasts has been studied for both markets. To achieve the empirical objective, a large number of models(1,447,740 in number) are estimated to produce load forecasts on a daily rolling basis. The forecast performance has been compared with the other models proposed in the literature. Among the linear models, for all window sizes of training data, the forecast accuracy of the combination model is better than the model selected with the minimum Akaike information criterion (AIC) and Bayesian information criterion (BIC) in both frameworks. Moreover, the ensemble of RBFN and linear models produces the best forecast. The results pinpointed that the proposed model’s precision and stability are higher than the earlier forecasting models proposed for both markets. The novelty in the model is that only a single hourly time series is used for forecasting, and there is no need for other explanatory variables.

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. Backtracking search algorithm as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information and support vector machine. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

Recent advancements in battery technology have made them more economically viable than ever before, making them suitable for various grid-scale applications. Due to their rapid response, batteries are attractive for providing ancillary services (AS), such as frequency regulation and reserve services, to the bulk power grid. On the other hand, an electric vehicle (EV) is viewed as a moving battery; consequently, EVs are also suitable for those services. This research proposes a linear planning model for an Electric Vehicle Aggregator (EVA) within a distribution network (DN) to offer Ancillary Services (AS) to the bulk power grid. The model takes into account contract design by identifying optimal incentives or charging tariffs that Electric Vehicle (EV) owners would be willing to pay the EVA for charging their vehicles. Additionally, the model takes into account the size of the electric vehicle (EV) fleet as a crucial planning factor for EV aggregation that depends on the energy pricing set by the EVA. The proposed model is developed to maximize the overall profit of bidding capacities in the energy and AS markets while supporting the operation of an unbalanced DN by maintaining the DN limits. Simulation results and sensitivity analyses on the model have been carried out to support the investment model and investigate the change in the optimal solution across different case studies. Simulations show that the optimal charging tariff ( $\beta $ ) is ( $0.02~{\$}/kWh$ )when considering scenarios where Distribution Network (DN) limits, such as thermal and voltage constraints, are ignored for all EV participation versus $\beta $ relations. When including the DN, the optimal payoffs vary based on the relationship between the charging tariff and the number of participating EVs.

To standardize the market conduct of electricity sales companies and elevate the power market management level， it is essential to conduct a credit evaluation of these companies. Therefore， based on Box-plot and orthogonalization methods， a credit evaluation model based on an enhanced discrete Hopfield neural network （DHNN） is proposed. Firstly， factors influencing the credit levels of electricity sales companies are analyzed， and a credit evaluation index system， which includes 11 indicators such as basic information， foundational management， contract management， and transaction management， is established. The weights for these indicators are determined using the Delphi method. Secondly， outliers in the indicators of electricity sales companies are addressed to derive optimal credit scores， enabling an objective assessment of their credit levels. Finally， the feasibility of the proposed model is verified through case studies. The results indicate that the model can objectively and accurately evaluate the credit levels of electricity sales companies.

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.

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.

The existence of chaos is particularly relevant, as the identification of a chaotic behavior in a time series could lead to reliable short-term forecasting. This paper evaluates the existence of nonlinearity and chaos in the underlying process of the spot prices of the Spanish electricity market. To this end, we used daily data spanning from 1 January 2013, to 31 March 2021 and we applied a comprehensive framework that encompassed a wide range of techniques. Nonlinearity was analyzed using the BDS method, while the existence of a chaotic structure was studied through Lyapunov exponents, recurrence plots, and quantitative recurrence analysis. While nonlinearity was detected in the underlying process, conclusive evidence supporting chaos was not found. In addition, the generalized autoregressive conditional heteroscedastic (GARCH) model accounts for part of the nonlinear structure that is unveiled in the electricity market. These findings hold substantial value for electricity market forecasters, traders, producers, and market regulators. [ABSTRACT FROM AUTHOR]

In order to improve the operating benefits of the distribution network (DN) and reduce the energy consumption costs of small-micro industrial parks (SMIPs), a two-layer optimal electricity trading method for DN with SMIPs is proposed. First, based on the Stackelberg game, a multi-objective two-layer optimal trading model for DN and SMIP is established. In the upper layer, the DN agent is regarded as the leader, and a trading model is established with the goal of maximizing the profits of agents. In the lower layer, an energy optimization model is proposed for the SMIP operators, which are regarded as the followers, with the goal of minimizing the operating costs. According to the buying and selling electricity prices at the upper and lower layers, a dynamic pricing strategy is formulated. The Karush-Kuhn-Tucker condition (KKT) is introduced to transform the two-layer model into a single-layer model, and based on linear transformations, the model is further converted into a mixed-integer linear programming model. The transformations aim to address the non-linear issues arising from multivariable coupling between the upper and lower-layer trading models. The simulation results show that the trading strategy proposed in this paper can effectively increase the profit of DNs while reducing the operating costs of SMIPs and can provide a reference for decision-making in the electricity market (EM) with the participation of SMIP. [ABSTRACT FROM AUTHOR]

This research incorporates an electricity market model based on a stochastic allocation of distributed resources and the analysis of an optimal demand response for a smart microgrid. This research develops a methodology that allows the application and comparison of various demand-response mechanisms and the analysis of the differences between them and the case of no-demand response, emphasizing economics, environmental care, demand curves, and other factors. By enabling more active participation by residential users of the smart microgrid, these demand-response methods help to flatten the demand curve and support the goals set by the electricity market model. Both conventional and non-conventional generators compete in the electricity market, with renewable energy sources preferred to encourage green generation. Conventional generators are required to supply electricity gradually, starting with the lowest pollution level. In addition, conventional generators are compensated for dispatch, system reliability, and availability. In addition, random variables are used in this study to predict initial load, solar radiation analysis, and biomass input before resources are optimized to meet demand. [ABSTRACT FROM AUTHOR]

The growing share of renewable energy sources in the energy mix and the liberalization of electricity markets has drastically affected the operation of electricity generators. Especially, in the last decade, fossil fuel-based generators have shifted their role from providing continuous base load to covering the peak demand and providing backup capacity to stabilize the grid. At the same time, a large amount of storage capacity is foreseen to be integrated into electricity grids in the coming years to shave demand peaks, mitigate price volatility, and provide services to the grid. In such a situation, in order to properly manage these crucial technologies, and thus guarantee the economic viability of the operation, it is essential to properly optimize the dispatch and define the best scheduling. This paper considers a gas turbine combined cycle and battery energy storage to study the problem of dispatch optimization of both generators and storage technologies. Different optimization algorithms have been considered and mixed integer linear programming is selected for its ability to identify the global optimum and the reduced optimization time. The impact of optimization windows (i.e., the forecast horizon of electricity prices) is also investigated. It is highlighted that an increase in forecasting ability, at least up to 36 h, guarantees more effective scheduling; on the other hand, it may require a significantly longer time. Subsequently, different approaches to account for the operation and maintenance costs at the optimization stage are assessed, and, finally, a sensitivity analysis is carried out with respect to market parameters (price average and variability) and technology features (conversion efficiency, cycle cost, etc.). [ABSTRACT FROM AUTHOR]

As China's electricity market continues to evolve, the increasing number and diversity of market participants have led to a rapid increase in data volume. An immediate challenge is designing a robust blockchain system that ensures not only the transparency, credibility, tamper-resistance, traceability, privacy, and security of substantial market data in a complex environment, but also provides trusted data support for intelligent dispatching within the power market. This paper proposes a solution to this pressing issue through Blockchain as a Service (BaaS). BaaS leverages the capabilities of cloud computing platforms to enhance the ease and efficiency of deploying and operating blockchain systems. Specifically, the paper introduces a BaaS system built upon an integrated intelligent scheduling and operation cloud-edge platform (CEP). It incorporates technological solutions such as data on-chain, trusted comparison, edge cluster data transmission, optimized transaction authentication, and lightweight storage nodes. Rigorous testing has conclusively demonstrated that the proposed BaaS system meets the requirements of the digital transformation of the China Southern Power Grid, and provides robust technical support for its future practical applications. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research investigates a grid with two areas interconnected by a high-voltage direct-current (DC) link. One of the areas, called the sending-end region, has intermittent renewable generation and frequency stability issues. To address the lack of frequency-regulation (FR) resources in the sending-end region of the interconnected grid, the participation of hydroelectricity–photovoltaics and pumped storage complementary systems (HPPCSs) in auxiliary frequency-regulation (AFR) services is studied in the context of the construction of the electricity market. Firstly, the HPPCS participating in AFR services considering DC modulation is modeled by combining the operational characteristics of the actual power station. Taking the purchase cost of auxiliary service as the objective function, the optimum allocation of FR scheduling demand is achieved by the proposed method. The simulations confirm that the proposed method of HPPCS participation in the AFR service of the sending-end grid can effectively maintain the frequency stability of the regional interconnected grid while ensuring optimal economic efficiency. The proposed method provides the optimal scheduling solution for multiple energy resources participating in the AFR service of the grid. [ABSTRACT FROM AUTHOR]

The large-scale introduction into traditional energy systems of generating facilities and equipment that use renewable energy sources as part of the implementation of the energy decarbonization strategy leads to a change in the structure of electricity production and consumption, a rapid increase in the number of participants in trade and economic relations on the electricity market. This determines the need for conducting research in the direction of creating easyto-use, functional object-oriented computer systems for modeling competitive market processes in terms of pricing management in order to increase the productivity and effectiveness of decisions made by market participants. The article formulates the principles, functionality and requirements for building such a class of computer modeling systems. Based on their application, the approximate composition of the functional modules of the computer system for modeling pricing processes with a single unified environment of information resources and computer models is determined. A deterministic algorithmic model is presented for the analysis of the dynamics of the volume distribution of electricity purchase and sale (demand) in the wholesale market segments as part of the computer models environment of the computer simulation system of price formation processes. The results of model calculations are presented for assessing the dynamics of demand in market segments, which are obtained using information resources and data presentation and visualization modules of the constructed computer simulation system. References 22, figures 2, table 1. [ABSTRACT FROM AUTHOR]

This study aims to examine Demand Side Response (DSR) programs, where the consumer side actively participates, to determine the most competitive, transparent, flexible, and suitable program for the structure of the Türkiye electricity market. The objective is to select one of the Demand Side Response (DSR) programs, using mathematical methods, that can provide the most effective response to Türkiye's increasing energy demand and present a market-specific proposal. The limited energy resources, losses in transmission, and environmental problems collectively drive the direction toward more efficient and economical energy use on the demand side (Benek Arslan, 2021). Therefore, various pricing programs and tariff strategies are implemented to regulate and balance demand (Özpınar, 2021). One of these pricing and tariff programs is the Demand Side Response (DSR) program, where consumption is reduced against fluctuations in energy prices in the electricity market or in case of system constraints and security (Hasanova & Varbak, 2021). The main objective of DSR programs is to offer energy to consumers at more affordable prices while ensuring an uninterrupted and sustainable energy supply. These programs promote energy efficiency among consumers, encouraging energy conservation and contributing to more sustainable energy consumption (Acar, Yule Bennett, & Scott, 2021). In this study, firstly, general information about demand side response (DSR) implemented in electricity markets is presented. The method section includes the analysis of DSR programs. The study aims to minimize the high prices in the market, system security, and transmission constraints by selecting one of the DSR programs and to encourage conscious consumption by managing energy consumption on the consumer side. [ABSTRACT FROM AUTHOR]

Administrative officials are often transferred during the course of their careers, yet little attention has been paid to how their transfer might affect energy transactions across regions. By employing data on the transfer of provincial leaders between provinces and inter-provincial electricity transactions in China, we examine the impact of local leaders' transfer on electricity transactions and the underlying mechanisms. The findings show that the transfer of local leaders leads to an increase in inter-regional electricity transactions in the direction of the transfer. This effect disappears when the transferred leaders leave office. The boosts are more accessible when a leader's previous and current jurisdictions are on the same regional grid, or at least adjacent to each other, or when the current jurisdiction embraces a heavier power load. Based on insights from political economy, these empirical findings can be attributed to the motivation of provincial leaders for career advancement and the absence of a fully competitive electricity market in China. Our study adds empirical evidence in support of the tournament theory of political promotion. It also provides novel insights into how developing countries might achieve a better allocation of their energy resources. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given the growing shares of renewable energy sources in the grids, the interest in energy storage systems has increased. The role of pumped hydro energy storage systems as flexible solutions for managing peak and off-peak prices from nuclear and fossil power plants in previous systems is now revitalized in the liberalized systems, with a volatile generation of wind and solar energy. Thus, understanding of the patterns behind the economics of energy storage is crucial for the further integration of energy storage in the grids. In this paper, the factors that impact the economic viability of energy storage in electricity markets are analyzed. The method of approach used in this study considers the electricity market price distribution, full load hours, the total costs of energy storage, and linear regression analysis. Using revenues from arbitraging a 10-megawatt (MW) pumped hydro storage system in the Western Balkans, resulting from the electricity market price distribution and the analysis of the total costs of storage, an econometric model is created. This model shows the impacting factors of energy storage development in the context of the rising renewables sector. Research shows that the previous hypothesis about the integration of energy storage systems in proportion to the increase in shares of renewables in the grids is incorrect. There is a significant correlation between energy storage revenues, the dependent variable, and the independent variables of hydro, wind, and solar generation. The conducted analysis indicates the future arbitraging opportunities of pumped hydro energy storage systems and provides useful insights for energy storage investors and policymakers. During the transitional period, until the deployment of renewables changes the effects of fossil power plants, energy storage price arbitrage is profitable and desirable for 500, 1000, and 2000 full load hours in the Western Balkan region. Despite the need for flexibility, with more renewables in the grids, large-scale energy storage systems will not be economically viable in the long run because of "revenue cannibalization". [ABSTRACT FROM AUTHOR]

The continuous integration of renewable energy sources into the EU's energy mix is reshaping the electricity market dynamics mainly due to the merit order mechanism affecting wholesale electricity prices (WHEP). This article aims to review the wholesale electricity market mechanism, identify the key factors affecting WHEP, and assess the extent of their contributions under different circumstances. Time series datasets, consisting of monthly observations of commodity prices and energy data regarding 25 EU members over the time horizons January 2015–December 2020 (pre-crisis) and January 2015–August 2023 (co-crisis), are used to support the theory, perform the comparison, and verify the validity of our hypotheses with the use of correlation and multiple linear regression analyses. Our empirical results show that in both cases, a 1% increase in the share of renewable electricity generation (RES) from one period to the next is ceteris paribus associated with an average of approx. 0.96% decrease in WHEP for the same period. However, extreme natural gas prices during times of distress significantly increase WHEP due to the merit order mechanism, from an average of 0.19% to 0.55%. This novel approach provides deeper insights into the interconnectedness of WHEP and the energy and environmental commodity prices and RES during changing economic and geopolitical circumstances, primarily highlighting the influencing factor of RES in WHEP developments. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology's business schemes are investigated. Furthermore, the challenges faced by the V2G technology's business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology's research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector. [ABSTRACT FROM AUTHOR]

This paper presents the impact of transmission congestion and demand side management on Electricity Producers’ market power using the quantitative investigation of pool-based market clearing mechanism. The mathematical problem formulation for pool-based market clearing consists of maximization of social welfare objective function, which incorporates maximization of demand benefit obtained from flexible demand bids and minimization of Electricity Producers’ real and reactive power costs obtained from actual and strategic supply bids. In this problem formulation, the nonlinear modeling of power system is used for real and reactive power flow equality constraints, transmission line capability inequality constraints (for incorporating the impact of transmission congestion), and generator capability curve equality constraints. The impacts of demand side management have been incorporated in the problem by considering single-period and multi-period demand flexibilities, and distributed generation placement at appropriate locations. The formulated optimization problem has been solved using nonlinear programming, which provides the nodal prices as byproducts of the solution. The electricity producers’ capability to exercise market power has been analyzed from their revenue and surplus indices, which have been obtained from nodal prices. Various case studies have been simulated on IEEE 30-bus system for quantitative determination of electricity producers’ market power. The results obtained are very interesting and demonstrate demand side strategies to tackle the electricity producers’ market power with actual and strategic bidding under normal and congested system conditions. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed. [Display omitted] • A model is developed for electricity procurement of hydrogen fuel stations (HFSs). • The uncertainties in demands and market prices are considered. • HFS procures electricity by day-ahead and balancing market and bilateral contracts. • Impact of different markets and transactions on HFS profit is assessed. • Sensitivity of HFS profit to weight of risk metric is assessed. [ABSTRACT FROM AUTHOR]

The current energy crisis calls for a rapid transition to renewables. Solar photovoltaics (PV) is the key component of the future energy system and a majority of PV systems are installed on rooftops in Europe. This article studies the business case of prosumers, i.e., consumers who produce part or all of their electricity with PV. The levelized cost of PV electricity is compared with the average value of PV electricity for the prosumers in residential, commercial, and industrial installations in Finland, Germany, France, Italy, and Spain with different self‐consumption (SC) and weighted average cost of capital rates. Prosumer business cases are positive in every studied market segment and country, especially for industrial PV systems with 100% SC where a payback time of less than 4 years can be achieved. The sensitivity of payback time on key input parameters is also studied. Reasonable cost of capital and adequate SC of PV generation remain the key elements for attractive prosumerism. [ABSTRACT FROM AUTHOR]

The multi-dimensional vector autoregressive (VAR) time series is often used to model the impulse-response functions of macroeconomics variables. However, in some economical applications, the variable of main interest is the product of time series describing market variables, like e.g. the cost, being the product of price and volume. In this paper, we analyze the product of the bi-dimensional VAR(1) model components. For the introduced time series, we derive general formulas for the autocovariance function and study its properties for different cases of cross-dependence between the VAR(1) model components. The theoretical results are then illustrated in the simulation study for two types of bivariate distributions of the residual series, namely the Gaussian and Student's t. The obtained results are applied for the electricity market case study, in which we show that the additional cost of balancing load prediction errors prior to delivery can be well described by time series being the product of the VAR(1) model components with the bivariate normal inverse Gaussian distribution. [ABSTRACT FROM AUTHOR]

The publication is devoted to the method of preliminary analysis of the results of the electricity market coupling in the "day-ahead" market segment. The relevance of the task of developing methods for preliminary evaluation of the results of the coupling of the "day-ahead" market of Ukraine with European interstate electricity exchanges is substantiated. The theoretical basis for the procedure for the selection of a separate external factor, with the aim of numerically evaluating the influence of such a factor that affects the change in the market equilibrium, is presented. The main components of the mathematical apparatus for finding market equilibrium in coupled electricity markets with the use of net export functions are described. The foundations for building algorithms for finding optimal solutions are determined. Features of the application of the proposed method on the example of the analysis of the results of Ukraine and Moldova electricity market coupling are demonstrated. Approaches to the creation of the function of Moldova's net export are shown, taking into account that the current structure of the Moldovan electricity market does not provide for the operation of the " day ahead" market segment. An example of simulation modeling of the processes of Ukraine and Moldova electricity market coupling for two typical hours is given. An example of the analysis of simulation results and preliminary conclusions regarding the consequences of Ukraine and Moldova electricity market coupling is shown. References 20, figures 3. [ABSTRACT FROM AUTHOR]

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.

Under the guidance of the 'dual carbon' goals and 'rural revitalization' strategy, the development of microgrids primarily based on wind, solar, and biogas energy is rapidly advancing in rural areas. A critical and challenging area of current research is how to optimally configure the capacity of these microgrids of varying sizes, taking into account the availability of resources in the system's environment and specific climatic conditions, to maximize economic benefits. Based on this, the article constructs a model of a hybrid AC/DC microgrid system powered by wind, solar, and biogas energy. It undertakes multi-objective optimization to achieve the highest utilization of renewable energy, the most economical cost, and the minimum carbon emissions while ensuring the reliability of the system's power supply. The study explores the economically and technically optimal configuration of this microgrid energy system under certain climatic conditions. The results indicate that the optimal configuration for a rural microgrid powered by wind, solar, and biogas energy should include a 2.6 kW biogas generator, 30.00 kW solar panels, 5.24 kW wind turbines, a 2.6 kW battery storage system, and a 10.00 kW bidirectional inverter. This configuration results in the lowest total net cost of the system, achieving optimal outcomes in terms of total net cost, cost per kilowatt-hour, and supply reliability.

The present study aimed to develop a model for determining an optimal bidding strategy for electricity producers, including the recommended selling price and the amount of electricity to be offered for participation in both the competitive electricity market and the energy exchange market. Hourly bids are suggested for the electricity market, while a monthly package, comprising peak load, medium load, low load, and base load, is proposed for the exchange market. By modeling a self-scheduling problem, the study aimed to develop optimal power production plans that maximize net profit over a one-month period. The research approach involved mathematical modeling using mixed-integer non-linear programming, which was performed in Lingo software and then validated in terms of effectiveness through an application to the case of a thermal power station. Relying on fuzzy necessity, credibility, and possibility, the research presented a robust model against the uncertainty of price with an adjustable level of robustness. Sensitivity analysis and the simulation approach were used to validate the performance of the model, demonstrating that the optimal response from the robust model, compared to the deterministic model, can maintain its efficiency in the face of fluctuations in the parameter of price uncertainty. Furthermore, the findings indicated that offering a base load package on the energy exchange market can yield a higher net profit value for the producer. Finally, the fuzzy interest rate and decision-making based on fuzzy goals were also examined.1. IntroductionIn recent years, researchers have directed their attention toward robust optimization in markets with uniform pricing systems. However, the application of robust methods in pay-as-bid systems remains unexplored. Therefore, a notable research gap exists, specifically in the robust optimization of pay-as-bid systems in the Iranian electricity market. Moreover, with the establishment of the energy exchange market in Iran, the simultaneous bidding, in both the energy exchange market and the day-ahead electricity market, has surfaced as a significant gap in existing research literature. In this respect, the present study contributes to relevant research by addressing existing gaps while considering the specific needs of the Iranian electricity market. The study tried to model the self-scheduling problem of an electricity producer to determine an optimal and robust strategy. Employing fuzzy theory to address the uncertainty of the market clearing price parameter, the model can protect the producer from electricity price uncertainty in the market, as well as foster a more secure environment for participation in competitive electricity markets.2. Materials and MethodsAs an applied and developmental research, the present study aimed to develop robust optimization models for bidding in the electricity market. This descriptive–analytical study examined, described, and explained uncertainty in decision-making, employing a fuzzy approach to tackle uncertainty. The research involved the mathematical modeling of the problem of determining the bidding strategy for electricity producers, presented as mixed-integer programming. First, the variables and parameters of the modeling process were introduced, followed by presenting the problem formulation. Subsequently, the implementation and its procedural steps were performed in the Lingo software to validate the effectiveness of the proposed model by applying it to the case of a thermal power station. 3.Results and DiscussionThe research proposed a model designed to address bidding challenges encountered by a price-taker electricity producer. The model centers on optimizing simultaneous monthly bidding in both the day-ahead electricity market and the energy exchange market. The objective is to optimally allocate the producer’s capacity between these two markets to maximize profit. To handle the uncertainty of electricity prices, a robust method is employed, necessitating estimates of the next day’s market price and the energy exchange price for the upcoming month. The proposed model underwent testing across various modes, including base load, off-peak load, medium load, and peak load packages. The results revealed that the producer’s profit is maximized when offering the base load package to the energy exchange market, followed by the medium load package. Peak and off-peak packages ranked third with equal values. Therefore, it is recommended for producers seeking participation in the energy exchange market to consider offering a base load package.4. ConclusionThe present research employed a robust fuzzy technique to deal with the volatility of electricity market prices, allowing decision-makers to make firm decisions with an adjustable level of robustness. The results of the proposed method indicated that the possibility criterion adopts an optimistic stance towards the settlement price, thus suggesting prices at higher levels than the necessity and credibility criteria. This criterion is suitable only when market signals indicate a potential price increase. In contrast, the necessity criterion adopts a cautious approach, showing robustness even at low confidence levels. This approach is well-suited for risk-averse decision-makers and scenarios where market signals point towards a potential price reduction.

Today, the rapidly increasing share of renewable energy sources requires a suitable mechanism to promote the development of renewable energy is urgently needed. To solve this problem, the paper will conduct a study to find out the incentive mechanism to promote the development of renewable energy with participation in the electricity market. The article compares different models of Fixed feed-in premium with CAP and FLOOR (month, week, day, and cycle) to point out the advantages and disadvantages of each model, thereby providing comments and assessing the impact of the models on the revenue of renewable energy plants. The result shows that the monthly average revenue of FIXED FIP with CAP and FLOOR (0.19-0.23 million VND/MWh) is less variation than half-hourly (0.16-0.23 million VND/MWh) and daily revenue (0.18-0.23 million VND/MWh). Moreover, the monthly model can mitigate the variation of spot price and cop with the payment period (monthly).

The significant increase in the installed capacity of power plants with renewable energy sources and the imbalance of the financial system of the wholesale electricity market of Ukraine prompted the Ministry of Energy to develop an alter-native support mechanism for RES producers. The introduction of a feed-in tariff (FIP), which compensates for the difference between the actual sale price of electricity and the "green" tariff, can help producers receive more money immediately after the electricity is released. However, studies have shown that exiting a balancing group without form-ing a new one can lead to increased costs associated with forecasting error. It is important for manufacturers to form independent balancing groups to compensate for negative consequences. The findings of the article show that there is no single optimal balancing group for all manufacturers, but some groups are often repeated. Switching to a separate balancing group can have a significant economic effect for the manufacturer, reducing the cost of forecasting error compared to being solely responsible for the imbalance. However, the balancing group determined by the method of retrospective calculation of the cost of the forecast error is not stable in the long term. Ref. 8, fig. 2, tab. 3.

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.

Abstract To facilitate wind energy use and avoid low returns, or even losses in extreme cases, this paper proposes an integrated risk measurement and control approach to jointly manage multiple statistical properties of the expected profit distribution for a wind storage system. First, a risk-averse stochastic decision-making framework and multi-type risk measurements, including the conditional value at Risk (CVaR), value at risk (VaR) and shortfall probability (SP), are described in detail. To satisfy the various needs of multi-type risk-averse decision makers, integrated risk measurement and control approaches are then proposed by jointly considering the expected, boundary and probability values of the extreme results. These are managed using CVaR, VaR and SP, respectively. Finally, the effectiveness of the proposed risk control strategy is verified by conducting case studies with realistic market data, and the results of different risk control strategies are analyzed in depth. The impacts of the risk parameters of the decision maker, the energy capacity of the battery storage and the price difference between the day-ahead and real-time markets on the expected profits and risks are investigated in detail.