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The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The first proposed method considers any norm for functional data to measure the distance between curves, the second one is designed to take different variabilities along the curve into account, and the third one takes advantage of the notion of depth of a functional data. The regression model with functional response on which our proposed prediction regions are based is rather general: it allows to include both endogenous and exogenous functional variables, as well as exogenous scalar variables; in addition, the effect of such variables on the response one is modelled in a parametric, nonparametric or semi-parametric way. A comparative study is carried out to analyse the performance of these prediction regions for the electricity market of mainland Spain, in year 2012. This work extends and complements the methods and results in Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018) (focused on prediction intervals), which use the same database as here.

The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids.

[Display omitted] • A hybrid DL model (CNN-BiLSTM-AR) is proposed to forecast the electricity price. • Integrating an AR model in parallel with a CNN-BiLSTM enhances the performance. • The influence of incorporating HPO methods like GA, PSO, and RS is investigated. • Performance difference between the models is verified using a statistical test. • Two distinct European datasets used to validate the proposed model performance. The inherent volatility in electricity prices exerts a significant impact on the dynamic nature of the electricity market, shaping the decision-making processes of its stakeholders. Precise Electricity Price Forecasting (EPF) plays a pivotal role in enabling energy suppliers to optimize their bidding strategies, mitigate transactional risks, and capitalize on market opportunities, thereby ensuring alignment with the true economic value of energy transactions. Hence, this study proposes an advanced deep learning model for forecasting electricity prices one day in ahead. The model leverages the synergistic capabilities of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory networks (BiLSTM), operating concurrently with an autoregressive (AR) component, denoted as CNN-BiLSTM-AR. The integration of the AR model alongside CNN-BiLSTM enhances overall performance by exploiting AR's proficiency in capturing transient linear dependencies. Simultaneously, CNN-BiLSTM excels in assimilating spatial and protracted temporal features. Moreover, the research delves into the implications of incorporating hyperparameter optimization (HPO) techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Random Search (RS). The effectiveness of the model is evaluated using two distinct European datasets sourced from the UK and German electricity markets. Performance metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), serve as benchmarks for assessment. Finally, the findings underscore the notable performance enhancement achieved through the implementation of HPO methods in conjunction with the proposed model. Especially, the PSO-CNN-BiLSTM-AR model demonstrates substantial reductions in RMSE and MAE, amounting to 16.7% and 23.46%, respectively, for the German electricity market. [ABSTRACT FROM AUTHOR]

This work proposes a methodology to calculate the price of the single pan-European day-ahead electricity market through an iterative process between the markets of two zones: the Iberian Peninsula and the rest of Europe. The motivation for this decomposition-based market clearing procedure is twofold: on the one hand, the two zones considered are weakly connected, due to the peninsular nature of Iberia, electrically connected to the rest of Europe only through the few tie-lines between Spain and France; on the other hand, the types of bids and related constraints are quite heterogeneous in both areas. The proposed algorithm has shown to provide virtually the same results as the official EU Pan-European Hybrid Electricity Market Integration Algorithm (EUPHEMIA), while improving convergence rates and often reducing solution times. The aim is not to replace EUPHEMIA, but rather to provide a more robust alternative that can be run in parallel, capable of providing a solution in cases of difficult convergence caused by the interactions of the complex constraints arising in the markets involved. [ABSTRACT FROM AUTHOR]

The characterization of the competitive interdependence among power consumers in power trading system contributes to pursuing efficient power supply and suppressing peak–valley load differences. Even though Stackelberg–Nash game approach has been applied to retail electricity trading with a single utility company and a group of end-users under competitive interdependence, the extension to multi-cluster retail electricity markets is not focused yet and the best strategies of end-users are still decoupled to each other. To bridge this gap this paper investigates the price-based demand response problem in multi-cluster retail electricity markets to coordinate the energy consumption behavior of the end-users and the retail price behavior of the utility companies. Adopting the structure of hierarchical game theory, the cluster retail price is controlled by the utility company to affect the energy consumption demand of end-users within the corresponding cluster and hence increase the income of both the utility companies and the end-users. In order to describe the coupled competition relationship among the bottom end-users in the system, the Cournot competition model of the differentiated oligopoly market is constructed by taking energy consumption as the individual strategy of end-users. The existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system are proved, and a seeking algorithm is proposed to seek the Stackelberg Nash equilibrium, which is regarded as the extension from the existing algorithm for multi-cluster setup. This work gives an insight to theoretically check whether the utility companies need to redesign the contract with the power generator to ask for larger maximum supply. • A power-system trading model for multi-cluster retail electricity markets is presented, where both the utility function and its best-response strategy are mutually independent of the game participants. • Using the backward induction, the existence and uniqueness of Nash equilibrium and Stackelberg Nash equilibrium in such a hierarchical game system is shown with a proper seeking algorithm. • The influence of the retail price of the utility company and the cap price of end-users in multi-cluster retail electricity markets are examined for the first time using a Cournot oligopoly game. • The proposed model helps to theoretically check whether the utility companies need to redesign the contract with power generator asking for a larger maximum supply. [ABSTRACT FROM AUTHOR]

• This paper studies the new problem, EVs' provision of flexible ramping products (FRPs). • The dispatching capability of EV fleets is disclosed through the feasible operating region in variable spaces. • A cloud-edge collaborated market structure for the electricity market with FRPs is proposed to facilitate a large amount of EVAs integrated with protected privacy. • A distributed optimal model combining the EVA's bidding subproblem and the market clearing subproblem is established. • The distributed optimal model is solved based on the modified analytical target cascading (ATC), in which the convergence performance is improved through heuristic constraints. • Time-coupled multi-period (TCMP) operation mode is implemented in the market to maximize the dispatching potential of EV fleets. More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs' dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively. [ABSTRACT FROM AUTHOR]

• The trading rules for cross-border electricity markets are designed. • A multilateral alliance game model based on non-cooperative game is built. • The influence of each variable parameter on the Nash equilibrium point was analyzed. 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. [ABSTRACT FROM AUTHOR]

• A Stackelberg game-based incentive mechanism embedded with a discharge guidance strategy for Private Electric Vehicles (PREVs) is proposed, enhancing the PREVs participation in load restoration of distribution system. • The V2G Station Operator (VSO)'s discharge guidance strategy for PREVs is modelled as an optimization problem using Huff attraction model, accounting for PREV user destination choice behaviors and preferences. • A Stackelberg game model is constructed to achieve an equilibrium of interests among multi-stakeholder and ultimately achieve load restoration. The uniqueness of the game equilibrium solution is proved. • The restoration results under different strategies, preference coefficients and electric vehicle response rates are investigated. Effectively utilizing large-scale Private Electric Vehicles (PREVs) in load restoration can enhance the capability of Distribution System Operators (DSOs) to respond to accidental power outages. However, the challenge remains in incentivizing and guiding PREVs to participate in load restoration. This paper proposes a Stackelberg game-based incentive mechanism, with a V2G Station Operator (VSO) discharge guidance model based on the Huff attraction model embedded to address this challenge. The VSO is introduced to manage the issue of PREVs having small individual capacities, and not meeting the criteria for direct participation in electricity markets. Firstly, a framework for involving PREVs in load restoration is introduced, clarifying the responsibilities of PREVs, VSO, DSO, and Distribution Market Operator (DMO), as well as the interrelation of incentive mechanism and discharge guidance strategy. Secondly, models for each game participant are established. The VSO encapsulates the discharge guidance strategy into an optimization model using the Huff attraction-based model that captures the destination choice probabilities of PREVs, and the DSO develops a load restoration model. Thirdly, a Stackelberg game model is established with the DMO as the leader and VSO and DSO as followers. The existence and uniqueness of the proposed Stackelberg game's equilibrium solution are proven, and a distributed iterative algorithm is employed for the solution. Finally, numerical simulations are performed with the IEEE 33-bus distribution system and a simplified road network to validate the proposed strategy's effectiveness in guiding PREVs' participation in load restoration, enhancing the restoration capability of the DSO, and improving the economic efficiency of each participant. [ABSTRACT FROM AUTHOR]

The electricity market transitions from a market clearing in which flexible generation meets inflexible demand to one in which inflexible generation meets flexible demand. Consequently, new types of flexibility are needed to balance generation and demand. This paper mainly aims to analyse competition and synergy between various flexibility options in the electricity market. The developed European electricity market model maximises the joint socio-economic welfare of the electricity and the hydrogen market. Transmission grid,- storage expansion, electric vehicle demand side flexibility and hydrogen sector coupling are flexibility options. This work examines the implementation of different flexibility options in the bidding zones Austria and Germany. Austria, with a very high storage capacity in the alpine arc and Germany, with massive wind turbine capacities serve as representative case study. Results show that the storage location heavily influences the corresponding socio-economic welfare increase. Hence, location-dependent synergy and competition between storage and transmission line expansion can occur. In contrast, sector coupled hydrogen production and storage expansion compete with each other as both will reduce very low electricity prices. Demand-side flexibility primarily benefits end-users while producer surplus reduces. In combination with storage expansion, further positive synergistic effects for end-users occur. The combination of storage expansion and demand-side flexibility can replace transmission line expansion and vice versa if used comprehensively. Further efforts could address the evaluation of these effects if the individual flexibility providers aim for something other than a system-wide but individual optimum. • Implementation of several flexibility options into the European electricity market. • Storage location and transmission capacity have a huge influence on the results. • Consumers benefit from combined storage and demand-side flexibility use. • Additional electricity demand by sector coupling increases renewable energy revenue. • Storage and sector coupling compete with each other as both reduce price volatility. [ABSTRACT FROM AUTHOR]

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. • We study the supply curves of several electricity markets in a five-year period. • We introduce a novel market-based distance to measure the curves' dissimilarity. • Clusters are provided with an insightful characterization of the resulting groups. • A distance-based learning procedure is proposed to forecast the supply curves. [ABSTRACT FROM AUTHOR]

• Mechanisms to encourage clean energy in Spain from 2005 to the present are analyzed. • FiT and FiP mechanisms can be effective in promoting new renewable projects. • Experience suggests that excessive support can jeopardize the entire market. • A transitional retribution scheme has proven to be effective if correctly supervised. 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. [ABSTRACT FROM AUTHOR]

• Presenting a multi-stage mechanism for managing energy interactions in renewable-based electricity grids. • Executing AS and regulation markets in day-ahead and intraday horizons, respectively, for more accurate resource allocation. • Assessing the technical and economic effects of the DSs participation in the SR market. • Evaluating the impact of ESSs, DR aggregators and V2G services on reducing regulation market costs. • Reducing system LMP and improving voltage profiles during peak period through the participation of DR aggregators, EV users and ESSs in the regulation market. The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids. [ABSTRACT FROM AUTHOR]

This study investigates a tri-level optimal market strategy for the integrated electric vehicle fleets and solar distributed generations (IEVSDG) to engage in the local electricity market (LEM) as a strategic price-maker. The distribution company (Disco) which operates the LEM, participates in the wholesale electricity market (WEM) to provide its consumers, is also a strategic price-maker. For this purpose, the IEVSDGs are integrated at the first level of the optimization problem, while the Disco operator and WEM operator form the second and third levels, respectively. In other words, Disco acts as an intermediary retailer that links the LEM (modelled by IEEE 69-bus distribution system) to WEM (modelled by an IEEE 24-bus transmission network). The study puts forward a novel solution strategy, where the second and third level problems are conjoined through the Karush–Kuhn–Tucker (KKT) conditions. Moreover, the equilibrium point between the first level and this conjoined problem is achieved through the alternating direction method of multipliers (ADMM). In a hybrid robust optimization (RO) and stochastic programming (SP) approach, the uncertain specifications, such as the arrival/departure times and daily travelled miles are modelled through the SP scenarios. On other hand, the RO was deployed to handle solar power forecasting uncertainties. Different case studies of dumb and smart charging were devised to evaluate the method. The outcomes show that the proposed three-level approach leads to 57.21% reduction in the LEM price, and 0.86% reduction in the WEM price. Furthermore, the smart charging strategy eliminated 105 MWh of load interruptions. • A tri-level optimization approach is developed to solve the energy market problem. • Both local and wholesale market behaviours are modelled. • The impact of uncontrolled and smart electric vehicle charging strategies on the market response is scrutinized. [ABSTRACT FROM AUTHOR]

• Combination of index system and improved SVM realizes identifying violations. • The KNBN and DBTC methods improve the efficiency of dynamic recognition. • The KNBN-CSSVM-DBTC strategy enhances the recall rate of the violation class. Accurate and real-time identification of market power abuse is a key task in the management of electricity market violations. However, there are few effective monitoring methods for extremely imbalanced datasets and progressively increasing amounts of data in actual market transactions. To address the aforementioned problems, this paper proposes an improved support vector machine by considering the index system, which can not only realize identification automatically but also minimize the credit risk of power market transactions. Firstly, the dataset is composed of an indicator system for measuring market power abuses. Secondly, a comprehensive algorithm for identifying offending data is proposed, which combines the K-Nearest Bound Neighbor and the distance between the means of two classes methods to overcome the shortcomings of traditional support vector machines with long training time due to the high dimensionality and progressively increasing amounts of data in actual market transactions, and the Cost-sensitive Support Vector Machine to tackle the problem of inefficient identification due to few tags in transaction data. Finally, five different features of constructed datasets and a power market synthetic dataset are tested, and results indicate that the proposed method can ensure high classification accuracy while significantly improving recognition speed and recall for violation data, which is more suitable for Chinese electricity market data and provide a dynamic detection method to identify market power abuse precisely and quickly. [ABSTRACT FROM AUTHOR]

The deployment of energy storage systems to the grid is expected to mitigate the effects of load imbalances caused by the variability of renewable energy sources. To motivate the investments in grid-connected energy storage systems, these projects must be economically feasible. This can be approached by optimizing the energy arbitrage scheduling process. A common approach is to consider this process as a stochastic optimization problem. Recent works tend to implement risk measures such as Conditional Value-at-Risk (CVaR) in the optimization process. In this work, the use of risk measures in the bidding strategy formulation is evaluated with a detailed simulation of a Battery Energy Storage System (BESS) performing arbitrage in the Iberian electricity market using historical data. A degradation model based on the Rainflow counting method is implemented to avoid neglecting battery ageing with usage. Results demonstrate that the addition of risk measures to a stochastic optimization approach yields poorer results than using a deterministic strategy such as a point forecast. • Energy prices must be forecasted when programming arbitrage operations on electricity market. • There are different ways to deal with uncertainties when scheduling arbitrage operations on day-ahead markets. • Literature uses risk measures when performing stochastic programming. • Analysis of such risk measures in a detailed simulation. • Analysis of degradation as a cost of use and its influence on the net-present value of a full project. [ABSTRACT FROM AUTHOR]

Wind power is one of the most important renewable energy sources worldwide. However, integrating these technologies in electricity markets faces challenges due to the intermittent wind speed, resulting in potential imbalances within power grids and subsequent inefficiencies. Traditional approaches for modeling the uncertainty simulate fixed wind power values in operational and planning models, which work well for the short term but are inadequate for medium-term decision-making due to lower forecasting accuracy and the inability to differentiate the imbalance risk faced in each simulated value. This paper proposes a novel methodology for incorporating wind energy imbalance risk into medium-term generation planning models. For this, we first simulate realistic wind power scenarios and perform a fitting distribution process to characterize the hourly wind power behavior. Then, we model the imbalance risk using quadratic-uncertainty cost functions for each simulated value. Subsequently, we conduct a temporal aggregation to reduce the computational burden, preserving the conceptual framework of the imbalance risk modeling. Finally, we propose a medium-term generation planning model that includes the imbalance risk model and the temporal aggregation strategy. Our approach is evaluated in a real-size case study formed by Spain, Portugal, and France's electricity markets, demonstrating accurate wind power imbalance risk modeling and lowered planning costs. • A new methodology to include wind energy imbalance risk in generation planning models. • Introducing a fitting process for modeling the distribution asymmetries of wind power. • Proposing a temporal aggregation method for efficient imbalance risk modeling. • The methodology is validated in Spanish, Portuguese, and French electricity markets. [ABSTRACT FROM AUTHOR]

The high penetration of distributed renewable energy resources (DERs) on the user side is essential for developing the low-carbon power system. Peer-to-peer (P2P) energy trading has emerged as an effective method to consume the DERs' surplus energy locally. However, the uncertainty of massive DERs poses challenges to the system operation. We thus propose a coupling market framework, where the P2P energy trading market can participate in both energy and ancillary service markets. Since the operation problems are mainly caused by uncertainty sources (i.e., PV generations or wind turbines), these independent uncertainty sources are first charged with uncertainty marginal prices derived from the distributionally robust economic dispatch models. These payments can be allocated to reserve providers. Then, the P2P energy trading is modeled as an equivalent federated power plant (FPP) to provide ancillary services and energy for the other market entities. The FPPs' reserve capacities are generated considering intra- and inter-FPP uncertainty. Finally, an extreme point scanning algorithm is developed to efficiently identify whether the FPPs can ensure the market operation and allocate the payments according to their reserve contributions. Case studies verify the theoretical properties and show the practicability of the proposed algorithms. [Display omitted] • The renewable energy uncertainty inside equivalent power plant models is considered. • The external characteristics of peer-to-peer energy sharing market are utilized to provide ancillary services. • The uncertainty marginal prices are developed to charge the uncertainty resources. • An enhanced absorbable region is proposed to measure the federated power plants' regulatory capacity. [ABSTRACT FROM AUTHOR]

Ambitious emission reduction targets require fostering more low-carbon technologies (LCTs) in distribution networks. Projections for future energy use predict a significant implementation of these technologies in residential areas. Despite this, individually they cannot effectively participate in electricity markets. This study examines the potential participation of residential LCTs (RLCTs) in multiple electricity markets, including wholesale day-ahead, real-time, and local energy markets (LEM), through the aggregators. We propose a stochastic weighted multi-range robust model to provide a strategy for RLCT aggregators to function as both sellers and buyers in these markets, as price-makers in LEM and price-takers in wholesale markets. The proposed model accounts for the uncertainty associated with the effect of offers/bids on the market clearing price of LEM and the availability patterns of aggregated LCTs. Results of a case study using realistic data reveal that the proposed approach results in higher overall profits compared to both risk-neutral and risk-averse robust methods. Furthermore, the introduced model is resilient to forecast errors, as evidenced by a 12% decrease in profits with the proposed approach compared to a 26% decrease with a risk-neutral strategy when the forecast error was increased by 20%. • A novel stochastic weighted multi-range robust optimization is proposed. • Comparative studies are conducted for the proposed and two conventional methods. • The proposed solution delivers higher profits while maintaining resiliency. [ABSTRACT FROM AUTHOR]

• Incorporating the advantages of the carbon quota system and discarding the shortcomings of the traditional carbon trading market, and constructs a coupled carbon-electricity market model with a high degree of linkage and information interaction based on this model. Due to the increasing problems of global warming and the gradual depletion of non-renewable energy sources, how to achieve low-carbon energy transition has become the focus of the entire energy industry. The article combines the policies related to carbon emission quotas and certified emission reductions in China, and designs a carbon credit trading mechanism that takes into account the synergy between electricity trading and carbon trading, based on which a carbon and electricity coupling market model is established with a carbon credit trading center as the core. Firstly, the carbon credit trading rules are prospectively formulated for the new power system, which connects power suppliers and power load users to form a diversified organic whole; secondly, the carbon credit management center is built with blockchain technology, which aims to shape the power spot market, power futures market and carbon trading market into a composite coupling, forming a new carbon and power coupled market with the carbon credit management center as the core, and the business interconnection and synergistic clearing of each market. Finally, a new carbon market model with carbon credit mechanism is established, and the components of nodal electricity prices are explored to quantify the impact of introducing carbon credit constraints on nodal marginal electricity prices. The results of the algorithm analyze the economic efficiency and social welfare of the electricity suppliers and load users, illustrate the necessity of introducing the carbon credit trading mechanism, and analyze the impact of the high proportion of renewable energy access on the carbon electricity coupling market. [ABSTRACT FROM AUTHOR]

• Proposing an equilibrium model for joint forward contracts and day-ahead markets. • Proposing a method for modeling the negotiations between producers and consumers. • Considering the strategic behavior of consumers in forward contract negotiations. • Considering the mutual impacts of forward contracts and day-ahead markets. • Proposing a risk management method based on the different concerns of market players. Forward contracts are one of the prevalent and useful tools for managing the risks associated with the volatility of the electricity market prices. Forward contracts and day-ahead electricity market are executed simultaneously, and hence, they affect each other. This paper proposes a comprehensive supply function equilibrium model to consider the mutual interactions between forward contracts and the associated day-ahead electricity market in a power system. Negotiation between each producer and consumer in forward market is taken into account in the model. In order to consider the risk management behaviors of all market players in the model, a new risk management method is presented. The proposed risk management method takes into account the concerns of market players about the future prices of day-ahead market over the delivery period. The model proposed in this paper is applied to a test system with forward and day-ahead markets. The results are compared with the case that there is no forward contract in the power system. Impacts of growing the concerns of producers and consumers about the future, impacts of increasing demand uncertainty, impacts of improving bargaining power of consumers in contracting period and impact of contracting obligations for producers on the simulation results are discussed. The proposed risk management method is compared with CVaR method and its efficiency is evaluated. Finally, applicability of the proposed model to real size power systems is examined. [ABSTRACT FROM AUTHOR]

The electricity market liberalization propelled by the increased operational flexibility observed in distribution systems urges the development of mathematical models for competitive electricity markets accounting for the participation of distribution systems operators. Most existing approaches model the interaction between the agents and the market as a bilevel programming problem, wherein the market is characterized at the lower level. Hence, the upper-level agent's objective function is favored over minimizing the costs of supplying the system's demand. In this paper, a new bilevel formulation is proposed. In the upper level, the market operator determines the optimal energy price that ensures the demand supply and maximizes the overall social welfare. In the lower level, the generation companies and distribution systems operators determine the amount of energy injected/extracted into/from the transmission system to maximize profits/minimize operational costs. A new pricing method is proposed in this paper as an alternative to the transmission system's dual variables, which are inaccessible under the proposed framework. The resulting nonlinear mixed-integer bilevel programming formulation is transformed into an equivalent single-level mixed-integer linear program. Numerical results illustrate the proposed approach's effectiveness in maintaining the minimum operational costs while distributing the payoff amongst the agents. • Bilevel formulation for wholesale markets with non-strategic price-maker agents. • New market framework with a clearing process that minimizes the total costs. • Development of a new pricing mechanism for electricity markets. • Energy storage systems and demand response minimizes the clearing prices. [ABSTRACT FROM AUTHOR]

• A novel multi-stage forecasting architecture is proposed for Short-term load forecast. • Consider error factors in short-term loading to improve model performance. • A novel ensemble method is proposed in the proposed forecasting model. • Use data processing technique to pre-process original data. • A MOEA/D algorithm based on angle and distance selection method and adaptive population generation strategy is proposed. Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single prediction model and the useful information in the error factors, resulting in low prediction accuracy. Therefore, this paper proposes a multi-stage integrated model based on decomposition, error factors, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The proposed model consists of three stages: in the first stage, the gated recurrent unit (GRU) is used to predict the components of complete ensemble empirical modal decomposition with adaptive noise, and new data sets are obtained by combining them with the original data sets to fully mine the data characteristics. In the second stage, the MOEA/D based on angle and distance selection strategy and adaptive population generation strategy is used to optimize GRU network parameters with accuracy and diversity as the objective functions, obtaining several load forecasting models and error forecasting models that consider accuracy and diversity. In the third stage, a new nonlinear integration method based on GRU optimized by MOEA/D is used to integrate load forecasting values and error forecasting values, considering error factors to further improve forecasting accuracy. Experimental results on the Australian wholesale electricity market and energy market datasets show that the proposed model outperforms the comparative model in terms of accuracy and generalization and can be widely applied in load forecasting. [ABSTRACT FROM AUTHOR]

This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability. • Develop a Stackelberg—Nash game model to characterize the interactive relationship. • Proof the existence and uniqueness of Stackelberg–Nash equilibrium. • Propose a distributed iterative algorithm with an adaptive step size. [ABSTRACT FROM AUTHOR]

• Siting model that directly incorporates a measure of uncertainty into decisions. • Proven that this model of the wind farm siting problem finds an optimal solution on the Pareto front. • Demonstration of accurate prediction of wind farm power output at a location without historical wind farm power data. Both increasing power output and minimizing the variability of that output are key factors in a highly utilized wind farm. Finding a location that offers this type of wind resource requires the use of stochastic methods to explicitly model the uncertainty of wind power output. While the literature is rich with wind speed resource assessments, this work moves the field to wind farm power assessment at undeveloped locations. A wind farm power prediction is achieved with improved spatial process representation. The power prediction results are utilized directly in a wind farm location siting optimization model which endogenously incorporates uncertainty in wind power output. The overall framework for siting new wind farms, termed the Farm Finder tool, is demonstrated in a case study in the Electric Reliability Council of Texas (ERCOT). It is shown that the formulation of this model will find an optimal solution to the siting problem on the Pareto front of high power output, with high reliability. Such location decisions improve the overall social welfare in the electricity market. [ABSTRACT FROM AUTHOR]

• A joint energy-frequency regulation power market is proposed for energy transition. • Environmental cost of fossil-fueled generation is incorporated into electricity trading. • Renewable energy generation is motivated to actively manage uncertainty and intermittency of outputs. • Extensive numerical simulations are conducted to validate the proposed system. Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes. [ABSTRACT FROM AUTHOR]

• A fully decentralized P2P power market with AC modelled network constraints is proposed. • Network operational security is guaranteed under proposed P2P market mechanism. • User privacy is protected as nodal voltage variables are the only public variables shared among participants. • Extensive numerical simulations are conducted to validate proposed models and solving algorithm. The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

Driven by environmental policies and cost reduction efforts, renewable energy sources (RESs) become increasingly popular worldwide. It is a promising way to integrate dispersed RESs in the form of aggregators into the electricity market. This paper focused on collaborative bidding for an aggregator that integrates wind, solar, hydropower and energy storage system (ESS) in day-ahead (DA) and intraday (ID) markets. We propose a comprehensive data-driven based information gap theory-robust (DIGDT-RO) to handle the multi-stage optimal bidding for the RES aggregator. The RO approach is presented to model the uncertainty of ID electricity price, while uncertainties related to wind and solar generation are considered in DIGDT, which allows the aggregator to adopt risk-averse or risk-seeking strategies towards generation fluctuations. In DIGDT, the forecasted error of wind and solar is estimated by a novel confidence interval-based ambiguity set construction method (CIAS), and then the possibility of hydropower and ESS compensating for power deviation is modeled by chance constraints. The numerical results verify the good profitability and superior adaptability of the proposed model towards uncertainties. • A multi-stage bidding model for the RES aggregator is developed. • A novel DIGDT model with chance compensation of hydropower and storage is proposed. • The characteristics are constructed by a confidence interval-based ambiguity set method (CIAS). • The coupled risk of DA and ID markets is managed. • The flexibility of controllable generators is fully exploited. [ABSTRACT FROM AUTHOR]

• A novel VCT-based bidirectional LA allocation method is proposed, along with a detailed analysis of its principles and implementation. • The VCT-based virtual contribution matrix is 40 times more efficient with computational complexity decreasing from O (n 4) to O (n 2 + n). • The VCT-based bidirectional LA method with 1.49% average relative error significantly enhances the accuracy and adaptability of power loss quantitative allocation. Loss Allocation is a crucial task in power transmission and distribution services, as it essential for reconciliation of generator-user settlement results. With the availability of distributed generation in recent years, power can flow in both directions along transformers and mainlines in the traditional distribution networks. This makes it difficult to calculate power losses in active distribution networks and correct transactions between generators and users. In this paper, we propose the concept of Virtual Contribution Theory and construct a virtual contribution matrix to illustrating how generators and loads use and occupy distribution networks. By combining Virtual Contribution Theory with Power Flow Tracing, a bidirectional loss allocation method based on the Virtual Contribution Theory is proposed to account for power losses in active distribution network. One key advantage of this method is that it eliminates many shortcomings of traditional loss allocation method, including cumbersome calculation processes, low solution efficiency, and limitations in bidirectional power flow calculations. This method can also fairly allocate power loss between generators and loads in the retail electricity market. We conducted tests on IEEE 33 and IEEE 69 systems to evaluate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Optimize investment decisions of transmission networks and the wind-storage hybrid power plant in the electricity market. • Develop a profit-driven wind-storage hybrid power plant planning model considering the ESS. • Reformulate the proposed tri-level planning problem into an equivalent bi-level problem. • Utilize the value-function reformulation and sample-based method for solving the equivalent bi-level problem with finite convergence. Coupling renewable energy sources (RES) such as wind farms with energy storage systems (ESSs) to form a hybrid power plant (HPP) has gained increasing attention. However, its investment profitability is closely associated with its capacity configuration, site location, grid network, and market-clearing outcomes. To study the HPP's planning strategy and its interaction with the transmission system operator (TSO), this paper proposes a tri-level coordinated planning scheme for transmission networks and a profit-driven wind-storage hybrid power plant (WSHPP) in the electricity market. The upper level is the transmission network expansion problem managed by the TSO, which responds to the investment decision of the WSHPP. The middle level determines the optimal capacity and site location of the WSHPP to maximize net benefits given transmission expansion schemes and prices from the third level. In the third level, the independent system operator (ISO) solves a scenario-based economic dispatch problem to provide locational marginal prices with fixed upper-level and middle-level decisions. The proposed tri-level hierarchical optimization model is computationally intractable because of its multi-level structure and binary/integer variables at the upper/middle level. A value-function reformulation and sample-based algorithm is implemented to solve the tri-level planning model and the convergence is proved. Numerical experiments on two typical test systems show the effectiveness of the proposed planning scheme and the tractability of the solution algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a two-layer optimization framework to co-optimize the P2P energy trading among multiple microgrids (MMGs) under uncertainty and optimal topology planning of the distribution networks (DNs). At the upper layer, the traditional verification optimal power flow model of DNs is transformed into a prosumer-focused and transaction-oriented dynamic network reconfiguration model. At the lower layer, uncertainty from wind power generations is integrated into the operating model of individual MGs and addressed by the stochastic programming (SP) method. Meanwhile, the conditional value at risk technique is introduced to find a trade-off between cost minimization and risk aversion flexibly. To establish the global negotiation mechanism among all participants (not only between distribution system operators and MGs, but also among MMGs), a fully distributed method is developed by combining an analytical target cascading algorithm and an alternating direction multiplier method. Furthermore, a diagonal quadratic approximation method is utilized to linearize the quadratic penalty term so that achieving parallel computing for all independent optimization subproblems. Simulations of different strategies, models, and distributed algorithms are implemented to verify the rationality and validity of the proposed method. The results of these case studies demonstrate that the proposed risk-averse SP approach can avoid over-optimistic solutions, the obtained P2P trading strategies are immune to uncertainty and P2P trading behaviors among MMGs can help reduce network losses of DNs. In addition, comparisons with other distributed algorithms verify the high performance of the proposed fully distributed method. • Protected the prosumer-focused and transaction-oriented natures of electricity markets. • Used a risk-averse stochastic programming method for effective solutions under uncertainty. • Designed a parallel computing algorithm based on diagonal quadratic approximation method. [ABSTRACT FROM AUTHOR]

Highlights • Novel electricity market negotiation strategy based on economic analysis. • Proposed methodology enables managing the negotiation risk. • Combination between internal data and sectorial data analysis. • Case study based on real data from the Iberian electricity market. • Proposed methodology achieves higher profits than state of the art approaches. Abstract Current approaches for risk management in energy market participation mostly refer to portfolio optimization for long-term planning, and stochastic approaches to deal with uncertainties related to renewable energy generation and market prices variation. Risk assessment and management as integrated part of actual market negotiation strategies is lacking from the current literature. This paper addresses this gap by proposing a novel model for decision support of players' strategic participation in electricity market negotiations, which considers risk management as a core component of the decision-making process. The proposed approach addresses the adaptation of players' behaviour according to the participation risk, by combining the two most commonly used approaches of forecasting in a company's scope: the internal data analysis, and the external, or sectorial, data analysis. The internal data analysis considers the evaluation of the company's evolution in terms of market power and profitability, while the sectorial analysis addresses the assessment of the competing entities in the market sector using a K-Means-based clustering approach. By balancing these two components, the proposed model enables a dynamic adaptation to the market context, using as reference the expected prices from competitor players, and the market price prediction by means of Artificial Neural Networks (ANN). Results under realistic electricity market simulations using real data from the Iberian electricity market operator show that the proposed approach is able to outperform most state-of-the-art market participation strategies, reaching a higher accumulated profit, by adapting players' actions according to the participation risk. [ABSTRACT FROM AUTHOR]

Automated negotiation plays a crucial role in the decision support for bilateral energy transactions. In fact, an adequate analysis of past actions of opposing negotiators can improve the decision-making process of market players, allowing them to choose the most appropriate parties to negotiate with in order to increase their outcomes. This paper proposes a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process. The proposed approach is based on an adaptation of the Q-Learning reinforcement learning algorithm to choose the best scenario (set of forecast contract prices) from a set of possible scenarios that are determined using several forecasting and estimation methods. The learning process assesses the probability of occurrence of each scenario, by comparing each expected scenario with the real scenario. The final chosen scenario is the one that presents the higher expected utility value. Besides, the learning method can determine which is the best scenario for each context, since the behaviour of players can change according to the negotiation environment. Consequently, these conditions influence the final contract price of negotiations. This approach allows the supported player to be prepared for the negotiation scenario that is the most probable to represent a reliable approximation of the actual negotiation environment. [ABSTRACT FROM AUTHOR]

Efficient provision of electricity requires timely expansions of power transmission capacity. However, regulation does not always send the right signals to generate the required (and timely) investments. Therefore, it is important to evaluate the effect of alternative regulations on investment on transmission capacity. In this paper, considering regulated remuneration, we perform this evaluation with a behavioral simulation model of the transmission capacity expansion, in which capacity is endogenously determined by the demand/supply relation. Two planning approaches were considered: centralized planning where the investments are fully coordinated by a central organism, and decentralized planning where the capacity expansions are driven by the investors’ rationality on the power market evolution. The model is applied to the Colombian case. The decentralized approach has lower costs (usage charges) than centralized expansion, but lower transmission capacity margins. As low transmission capacity margins create supply risks in high demand periods, regulators can increase coordination in decentralized planning by directly promoting investments that increase security of supply. [ABSTRACT FROM AUTHOR]

• Participation of consumers in generation, resulting in the formation of the electricity market concept. • Increased efficiency and utilization of system capacity, as well as reduced transmission congestion. • Power quality and reliability enhancement, with lower prices. A multi-microgrid system – containing various complementary energy sources – must coordinate the power flow among microgrids and the utility grid for stability and reliability enhancement, and also should obtain optimum energy management. Multi-agent-based optimization techniques are ideal for the management of interconnected microgrids, due to their decentralized nature and multifactorial approach in the decision-making process. In this paper, an optimization model is proposed for interconnected multi microgrids which considers the overall cost of the system. For optimum system operation, the hierarchical energy management system is utilized based on the multi-agent systems (MAS) theory. Each element of the system is considered an independent agent connected to others through the internet of things (IoT) platform. A key benefit of such a structure is its capability to be divided into numerous levels. Moreover, the communication burden of the system is reduced with different management layers. In this paper, efficient operation and management tools including the regulation power (for dispatchable DGs), independent DGs, and power exchange with the utility and adjacent microgrids are utilized for optimum operation. To obtain more realistic modeling, the uncertainty of renewables and demand is taken into account using decomposition-based modeling, which reduces the computational burden as well. Furthermore, demand response programs are also considered. Finally, a rule-based real-time corrective algorithm is presented to eliminate the mismatched power. The simulation and implementation results demonstrate the efficacy of the proposed MAS-based optimization model in decreasing the operational costs of the multi-microgrid system with minimal communication and computational burden. [ABSTRACT FROM AUTHOR]

This paper provides two procedures to obtain prediction intervals for electricity demand and price based on functional data. The proposed procedures are related to one day ahead pointwise forecast. In particular, the first method uses a nonparametric autoregressive model and the second one uses a partial linear semi-parametric model, in which exogenous scalar covariates are incorporated in a linear way. In both cases, the proposed procedures for the construction of the prediction intervals use residual-based bootstrap algorithms, which allows also to obtain estimates of the prediction density. Applications to the Spanish Electricity Market, in year 2012, are reported. This work extends and complements the results of Aneiros et al. (2016), focused on pointwise forecasts of next-day electricity demand and price daily curves. [ABSTRACT FROM AUTHOR]

Hydropower generation relies heavily on neighboring upstream plants due to the natural cascading structure in hydropower systems. In competitive markets, discharging decisions become more self-interested and opaque, making it difficult for downstream plants to predict inflows and make decisions. To address this scheduling information asymmetry (SIA) problem, this paper proposes a combination approach that consists of three parts: (1) constructing a bilevel model, in which the upper-level searches for optimal parameters and the lower-level model simulates the upstream plant's operating behavior with parameters provided by the upper-level model, (2) adopting the proposed parallel hypercubic searching algorithm (PHSA) to solve the non-convex bilevel model, and (3) accelerating the solving process using a searching domain reduction (SDR) method derived from engineering experience in the three-dimensional case. Case studies on the Lancang River show that the combination approach is efficient, accurate, and robust in estimating operational parameters and historical operating states of the rival's upstream plant. Kruskal-Wallis and Tukey-type tests indicate that the proposed combination approach outperforms alternative methods. • An inverse bilevel model is used to tackle scheduling information asymmetry problem. • The parallel hypercubic searching algorithm is proposed to solve the bilevel model. • A searching domain reduction method is proposed to accelerate the solving process. • Case studies in China indicate the solution is efficient, accurate, and robust. • Kruskal-Wallis and Tukey-type tests show the proposal superior over the alternatives. [ABSTRACT FROM AUTHOR]

In Vietnam, the reform of the electricity sector is an urgent need, unbundled and striking to begin a Competitive Electricity Market. The Vietnam electricity day-ahead market model is implemented based on the gross pool model with specific market rules and network constraints. Today, the Vietnam electricity market is operating at the Vietnam Wholesale Electricity Market step but is not yet completed. Due to the long shape of Vietnam territory, an implicit cross-compensation mechanism is applied to create a balance between customers leading to an obstruction to allowing multi-buyers in the electricity market. Therefore, the main aim of this paper is based on the official documents to propose the current mathematical model of the Vietnam electricity day-ahead market using a Mixed Integer Linear Problem, called the Single Buyer Day-Ahead Market model. Then, the Multi Buyer Day-Ahead Market model is implemented to allow multi-buyers to trade in the market. The idea of purchasing uniform prices for multi-buyers using in Italy could be also introduced in the Integrated European Electricity Market in case of large price variations between countries as a mechanism to uniformize the price of energy in Europe for consumers. In this paper, purchasing uniform prices is proposed to provide an explicit cross-compensation mechanism for Vietnam. Eventually, the proposed models are evaluated with four scenarios in two years representing typical days in Vietnam. The results confirm that, on one hand, the proposed market-clearing models can be applied in a real application for any expansion of the market, i.e. the number of suppliers, demands, and bidding areas; on the other hand, the purchasing uniform prices provide an explicit cross-compensation between bidding areas helping to enhance the fairness and the transparency of the market. • Proposed a full market model based on purchasing uniform prices for the Vietnam Wholesale Electricity Market. • A single optimization framework is used. • The model can sustain any change of bidding zones in futures. • Proposed model validation with a real data to confirm the ability of the application. [ABSTRACT FROM AUTHOR]

• "Operator-Aggregator-Consumers" joint framework is constructed and the aggregator's optimal trading strategy is analyzed. • To deal with market uncertainty, deep reinforcement learning is used to optimize selection and reward of the consumers. • The SOMs helps to realize dynamic consumer classification, improving the accuracy of response forecast and bid construction. Demand response plays a significant role in improving electricity market efficiency and keeping power system stability. Load aggregators can provide reliable resource for power balance and auxiliary services by integrating dispersed responsive loads. In order to meet the aggregators' requirement of timeliness and accuracy in the real-time demand response market, a method of perception and decision-making for demand response based on dynamic classification of consumer is proposed. The price elasticity of electricity demand is calculated based on continuously updated trading experience and applied as a classification criterion. Consumers are dynamically classified by self-organizing maps algorithm to perceive consumers' responsive ability. Furthermore, the interaction model of aggregator and consumers in market environment is constructed, and deep reinforcement learning is applied to solve the trading strategy in the uncertain market with incomplete information. Simulations show that the results of trading partner and reward price gained by the proposed method are appropriate. Therefore, the deviation between response quantity and the bid-winning volume is effectively controlled, and the transaction revenue of the aggregator is improved. [ABSTRACT FROM AUTHOR]

• A data-model hybrid driven approach is used to model the flexibility of the charging stations. • An ADMM-based clearing mechanism considering the carbon intensity control is proposed. • The flexibility of the charging stations is verified to be used to control the carbon intensity. A large number of high-capacity charging stations are being built and will enter the wholesale electricity market as independent entities. Energy storage is considered to be an effective means of mitigating carbon emissions. When a large number of charging stations with storage capacity enter the market, how to make full use of their flexibility in the wholesale market to control the carbon intensity of the power system is an urgent problem to be solved. To address this problem, in this paper, a day-ahead market clearing mechanism for nodal carbon intensity control using the flexibility of charging stations is proposed, and a distributed clearing algorithm based on the alternating direction method of multipliers is designed. The local distribution network operators are used as intermediate coordinators, through which the market operator and the local charging station operators transmit system flexibility demands and energy schedules, respectively. Market clearing is completed through several iterations. This reduces the complexity of the unified clearing model. Moreover, based on the data-model hybrid driven approach, the generalized energy storage model of the charging stations and the linear nodal carbon intensity constraints of the power system are established. The locational marginal price considering the carbon emission cost is derived. The simulation verifies that using the flexibility of the charging stations to improve the power flow distribution of the power system can control the nodal carbon intensity with only 0.11% cost increase. The distributed algorithm has good convergence. [ABSTRACT FROM AUTHOR]

• Aggregator model of demand-side energy resources are proposed. • Procurement cost is minimized for day-ahead market and an imbalance pricing system using DSRs. • The impact of PV forecast error on procurement cost is found to be largest. • Control saturates as the proportion of controllable demand-side energy resources increases. We propose a tool for aggregating a large number of buildings' electricity demand and photovoltaic (PV) generation, controllable heat pump water heaters (HPWH) and electric vehicles (EV) to minimize the procurement cost in a market transaction consisting of a day-ahead market and an imbalance pricing system. The novelty of this research is that the aggregator's strategy can be modeled including various uncertainties, and the value of multiple demand-side resources (DSRs) can be quantified within realistic market rules. The model shows that the ability to control the imbalance through real-time control of DSRs has significant economic benefits. In the control of HPWH hot water storage operation, the procurement cost can be reduced by 13% compared to nighttime operation when the same number of units are installed as the aggregate target rooftop PV and residential demand. The electric vehicle storage battery charge/discharge control can reduce the total cost by 38% compared to nighttime charging under the same conditions. The impact of the PV-prediction error on the procurement cost was found to be generally larger than those of other parameters, and the market-price-prediction error was found to be considerably large during EV charging and discharging optimization. Furthermore, the simulation confirms that the value of control saturates as the proportion of controllable DSRs increases compared to uncontrollable demand and generation. [ABSTRACT FROM AUTHOR]

• A stochastic offering approach for PV plants in DA and balancing markets is proposed. • Prediction of the competitors' behavior in the electricity market from an intended PV point of view is proposed. • The competitors' behavior in both DA and balancing markets is modeled based on practically available data. • The proposed method deals with a wide range of uncertainties that a PV is faced with. • DEC concept with a scenario-based approach for market participation of a PV is presented. Given the increasing share of photovoltaic (PV) power plants in power generation worldwide, this paper presents an optimal offering strategy based on artificial neural networks (ANNs) in both day-ahead (DA) and balancing markets. A PV plant faces a variety of uncertainties at the time of participating in the electricity market, including its production level, balancing market demand, market price, and offering behavior of the competitors. To manage all types of uncertainties together, a combination of scenario-based approach and decentralized equivalent competitor concept is proposed in a novel offering strategy for PV plants. The approach is implemented on the IEEE 30-bus system and two case studies, namely, with and without an energy storage system, are considered for result analysis. The results show a 9.6% of profit increase while the PV plant employs the proposed approach in comparison with the premium feed-in tariff method. Furthermore, in the case where the PV plant is equipped with battery storage with a charging capacity equal to 50% of the maximum production capacity of the PV plant, using the proposed approach for scheduling the battery, the profit has increased by 2.5%. To investigate the performance of the proposed approach in terms of robustness a numerical comparison is conducted. For this purpose, the proposed approach has been compared with the affinely adjustable robust offering strategy method in case of robustness. Numerical results show that the effectiveness of the proposed method in the worst case (maximum considered level of uncertainty) is 77% of the best case (without uncertainty), while this is 36% for the affinely adjustable robust offering strategy-based method. The conducted investigations show that the proposed offering approach, while efficient, is robust against a high level of uncertainties. [ABSTRACT FROM AUTHOR]

• A stochastic programming model is proposed to coordinately optimize the bidding curves of multi-regional monthly auction markets for hydropower stations serving multiple power grids. • A scenario-based method is developed to describe the uncertainties in runoff and multi-regional electricity prices with preserving their cross-correlations. • A monthly-rolling solution framework is proposed to solve the stochastic programming model so that the latest actual data can be dynamically considered to improve the accuracy of declaration. Driven by the development of the multi-level and unified electricity market in China, large-scale hydropower stations serving multiple power grids (LHSMPGs) have recently been required to enter into multiple regional electricity transactions. However, due to the multiple uncertainties of runoff and electricity prices, complex operation constraints, and difficulties in quantifying opportunity costs, formulating reliable bidding strategies for multi-regional monthly auction markets to maximize profits presents a challenge for LHSMPGs in China. Accordingly, in this study, we propose a tractable bidding optimization method for LHSMPGs in multi-regional monthly electricity markets. First, a scenario-based method is developed to describe the uncertainties in runoff and multi-regional electricity prices, as well as their cross-correlations. Then, a stochastic programming model is developed to coordinately optimize the piecewise bidding curves in multi-regional auction markets. The model considers the opportunity cost of bid quantities and complex monthly electricity allocation requirements during the whole time horizon of a year. Moreover, a monthly-rolling solution framework is designed to solve the stochastic programming model to ensure that the latest actual data can be dynamically considered to improve the accuracy of declaration. The application of the proposed method is verified through two cases based on the Xiluodu Hydropower Station, which ranks fourth in the world in terms of installed capacity. The results demonstrate that optimizing bidding curves in multi-regional monthly auction markets coordinately can enhance gross revenues and electricity sales. Moreover, a sensitivity analysis reveals that an excessively large or small allocation proportion of electricity between annual and monthly markets would weaken the guiding role of monthly auction markets in optimizing the allocation of hydropower resources and exacerbate the uneven distribution of hydropower generation between flood and dry seasons. [ABSTRACT FROM AUTHOR]

Redispatch measures are a key instrument of the Central European electricity market design to prevent congestion of several transmission lines after the market clearing. In addition to generation rescheduling, demand-side flexibility can reduce this redispatch need. This paper mainly aims to analyse the flexibility demand application of electric vehicle fleets in the redispatch market. The developed European electricity market model minimises dispatch- and subsequently redispatch costs whilst using electric vehicle fleets as flexible demand. A novel modelling approach allows the integration of this flexibility into large-scale linear programming models without losing their essential parameters. This case study is examined in Austria with a remarkably high share of electricity from renewable energy sources and different market penetrations of electromobility. Results show that integrating this system as a redispatch measure leads to a reduced curtailment of renewable energies (up to 25%), whilst less additional thermal power plant usage is needed. Furthermore, redispatch cost and the associated CO 2 emissions are reduced by 3.3% to 13.9%. By contrast, using this flexibility as a market-based charging strategy raises CO 2 emissions and redispatch costs drastically by 186% if 2 million electric vehicles are considered. Especially with the high electrification rate of the transport sector, the provided flexibility potential significantly impacts the electricity market. Further efforts could address the influence of demand-side flexibility not only on redispatch within one control area but also on cross-border redispatch and counter-trading. • Implementation of electric vehicle fleets as demand-side flexibility. • Integration of flexibility in the dispatch and redispatch of the electricity market. • Flexibility as redispatch measure reduces redispatch costs and CO 2 emissions. • Flexibility as market-based charging strategy leads to an increase in congestion. [ABSTRACT FROM AUTHOR]

• Developing a novel modeling of zonal congestion management method based on the demand response. • Introducing a new congestion index to form the flexible congestion control zone. • Determining the optimal incentives in the demand response programs based on the congestion alleviation. • Investigating the effects of dynamic lines rating on the congestion. Congestion is one of the main obstacles to fair competition in today's electricity markets. In this regard, a set of rules are legislated to create a fair competitive environment and ensure adequate control over consumers to maintain an acceptable level of reliability with maximum economic efficiency. In this paper, using power tracing techniques and sensitivity analysis, a new dynamic zonal congestion management (ZCM) model based on the demand response program (DRP) is developed. The congestion control zones are determined by an introduced congestion index (CI) including some candidates with both high effectiveness in congestion alleviation (the sensitivity analysis) and high responsibility in the congestion creation (the power tracing technique). Then, through the intelligent implementation of DRP in the zone, a conceptual framework with a comprehensive solution method for congestion alleviation is proposed. Throughout the proposed partitioning based DRP, the optimal amount of load decrease and incentives are determined considering economic issues. In power system operation, contingency analysis such as generator and line outages and uncertainty of lines capacities are inevitable, so they will be taken into account in our proposed methodology. The capacity fluctuates a lot due to the environmental conditions and it will be shown through a comparative study that dynamic line rating (DLR) has a significant impact on results. Using the proposed method, the system is simplified and power system operators can put their focus on the candidate zone than the whole network and alleviate the congestion effectively in the least possible time. The proposed methodology is applied on IEEE 118-bus test system and the results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Recently, there has been an urgent tendency to steer the transport system towards zero-emission. Along natural gas vehicles, electric and hydrogen vehicles represent a promising way to a low-carbon system. Since the development of these vehicles relies on advances of refueling stations, their economic and operational aspects need to be considered. Therefore, this paper addresses a multi-product charging station for refilling electric, hydrogen and natural gas vehicles. The station participates in day-ahead and intra-day markets where the market price is subject to the uncertainty. For charging station participating in sequential markets with different prices, it is necessary to consider the coordinated bidding. Taking into account the sequential clearing of these markets and the gradual realization of market prices, the bidding problem is formulated as a two-stage stochastic program. In order to solve this large-scale stochastic program with a huge number of scenarios, the rolling planning method in combination with sample average approximation and SCENRED tool are applied. The study aims to attain the optimal operation of devices and bidding curves of the charging station in order to maximize its profit. The obtained average optimality gap based on Monte Carlo sampling approach, which is less than 1%, proves the effectiveness of our proposed algorithm in solving this large scale stochastic optimization. The submitted bidding curves to the intra-day market are found to be dependent on changes in the day-ahead market prices. Tornado diagrams indicate that the selling price and demand of electricity are ranked as the most effective factors on profit. • A grid-connected, multi-product price-taker charging station is proposed. • Two-stage stochastic program for coordinated bidding of this station is developed. • A rolling horizon formulation for solving this two-stage program is employed. • Sample Average Approximation framework is implemented to reduce the problem size. • A GAMS package called SCENRED is applied to reduce the price scenario numbers. [ABSTRACT FROM AUTHOR]

• Modeling GenCos' CW in market equilibrium problem considering SCGMS. • Obtaining oligopolistic generation and maintenance schedules, consumption of loads, and nodal prices based on KKT conditions. • Capacity withholding analysis of GenCos using two novel indices WTI and RWLI. • Considering wealth transfer from loads to GenCos to model GenCos' CW impacts on electricity markets. • Introducing RWLI to model GenCos' CW by measuring loss of social welfare. The analysis of capacity withholding (CW) plays an important role in monitoring of the market power. One way of CW in oligopoly electricity markets is the strategic maintenance of generation companies. In this paper, a security-constrained generation maintenance scheduling is proposed using two sets of comparison indices to analyze the CW from the nodal and global points of view. The relative welfare loss index measures the market inefficiency in the form of social welfare loss relative to efficient markets, while the wealth transfer index enables the analysis of wealth transfer from customers to GenCos due to the CW. It is revealed that these indices identify the additional strategic leeway of GenCos due to the maintenance scheduling. Consequently, they can be used by the regulators and independent system operators for the evaluation of CW in the presence of GenCos' strategic maintenance. Focusing on changes in generation, nodal prices, and social welfare, the modified versions of IEEE 30 and 118-bus test systems are provided to demonstrate the robustness and effectiveness of the proposed indices. It is revealed that the price and welfare based measures detect the CW in the context of maintenance scheduling whereas the mere analysis of quantities is not sufficient. [ABSTRACT FROM AUTHOR]

Power dispatch is one of the basic functions in power systems operation and planning activities. It involves security, performance, and economic aspects, while maintaining generation and demand balance. The Optimal Power Dispatch (OPD) and the Optimal Reactive Power Dispatch (ORPD) are formulations of the power dispatch that respectively address objectives related to efficiency, and voltage regulation and stability. Under most common approaches, both problems are independently solved, despite their strong connection. This paper addresses the problem of the simultaneous dispatch of real and reactive power considering four conflicting objectives: minimization of network power losses, maximization of system voltage stability, maximization of power factor on transmission network step-down transformers, and minimization of power dispatch economics. The mathematical model is developed based on the current regulatory framework of the Brazilian electricity sector. To solve the problem, an optimization methodology using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed. This methodology is tested both on IEEE 30-bus and 57-bus systems and on a Brazilian 10351-bus system. For each system, a set of efficient solutions with good convergence and diversity is obtained. We highlight a 6.5% (30.1 MW) reduction of power losses in the Brazilian system, as well as a 2.8% reduction in the power dispatch cost and a increase of 0.6 in the power factor. The methodology showed that the simultaneous dispatch of real and reactive power is a successful approach to improving the performance of power systems in several criteria. • Many-objective methodology for real and reactive power optimal dispatch. • Estimating opportunity costs of synchronous generators in a deregulated power market. • Simultaneous optimization of real and reactive power dispatch. • Efficient procedure to solve large linear systems for power flow algorithms. [ABSTRACT FROM AUTHOR]

• Optimal number of generators taking part in congestion management problem. • Two variants of MBO with linear weighting factor and chaos embedded designed and used. • Variants are first validated on different unimodal/ multimodal standard benchmark functions. • A comprehensive assessment of congestion mitigation is investigated on different standard test systems. • The effectiveness of proposed MBO variants is compared with different existing optimization techniques. The emergence of competitive power markets in recent decades has paved the way for the enormous increase in the number of transactions between producers and consumers to satisfy the exponentially increasing load demand. These transactions usually occur through direct bilateral, multi-lateral agreements or some common platform. However, this operation puts additional strain on the transmission network, which serves as a conduit between producers and consumers leading to congestion. As a result, satisfying all the committed power transactions under congested transmission lines economically becomes infeasible. Independent System Operator (ISO) is an entity that is responsible for ensuring congestion-free power transactions to preserve the system's security and reliability. The objective of this work is to mitigate the congestion in the network while satisfying the system constraints resulting in more stable operation with reduced line flows and low system losses. The generator sensitivity factor (GSF) is utilized to single out the generating units that need to be rescheduled to alleviate transmission lines congestion. Further, the proposed variants of Monarch Butterfly Optimization (MBO) namely Improved MBO with linear weighting factor (IMBO-LW) and chaos-embedded MBO (CHMBO) are used to reschedule the generators with an aim to minimize the cost of rescheduling and the total change in power after rescheduling. Additionally, standard benchmark functions are used to validate the effectiveness of the proposed MBO variants and the same variants have been scrutinized on modified IEEE 30-bus and New England 39-bus test systems to alleviate the problem of congestion for secure and reliable operation of power system. [ABSTRACT FROM AUTHOR]

• The operational problems of interconnected FDNs requires efficient P2P trading method. • A two-layer P2P electricity trading method is proposed based on non-cooperative games. • The privacy preservation is enhanced by applying a modified market-clearing algorithm. • A P2P trading adjustment mechanism is designed to improve SOP profit. With the integration of power electronic devices represented by soft open points (SOPs), distribution networks have gradually evolved into interconnected flexible distribution networks (FDNs). Considering the deregulation of electricity market and user privacy, multiple stakeholders have participated in the operation of FDNs. Peer-to-peer (P2P) electricity trading is promising to alleviate operational problems of interconnected FDNs. As multiple regions pursue the maximum profits individually, non-cooperative game methods can be utilized to realize fair profit allocation in P2P trading. In this paper, a non-cooperative game-based P2P trading method is proposed to meet the electricity trading needs of multi-region interconnected FDNs. First, based on non-cooperative games, a two-layer P2P electricity trading framework is established to realize cost reduction and voltage profile improvement of multi-region interconnected FDNs. Then, a P2P trading adjustment mechanism is designed to improve the operational profits of SOP, in which spatial active power trading adjustment, temporal dispatching of energy storage (ES) link and reactive power support are incorporated. Finally, the effectiveness of the proposed method is verified based on a practical distribution network with four-terminal SOP in Tianjin. The results show that the proposed P2P electricity trading method can promote the economic operation performance of interconnected FDNs and improve the operational profit of SOP. [ABSTRACT FROM AUTHOR]

Under the vision of Carbon Neutrality, multi-energy networked micro-grids (MGs) becomes a reliable and clean solution to achieve this goal. High penetration of renewable energy resources (RERs) brings great uncertainties to stable operation. Uncertainties in day-ahead scheduling must not be ignored. Also, demand response (DR) is getting increasing attentions in modern electricity market. In this regard, this study proposes a two-time-scale dispatch strategy for networked MGs considering multiple uncertainties and DR events. Both electric and thermal demands are taken into account. This strategy encompasses two stages, uncertainties considered day-ahead stage and DR considered intraday stage. The former stage combines scenarios analysis and budgeted robust methods to provide a day-ahead scheduling plan with robustness. The latter stage is an online model to deal with the newly updated forecast data and DR events. Adjustment cost is employed to measure the rescheduling cost. Emission governance cost has already been considered in the both stages, which contributes to realisation of carbon-neutral society. Four cases are simulated to verify performances of the two-time-scale dispatch strategy. Results show this strategy can effectively provide stable energy supply in networked mode. There are 12.0 MWh electricity and 11.8 MWh heat transmitted among MGs to guarantee reliability. Compared with nominal dispatch, 4.34% in average surplus electricity will be generated for robustness. And 19, 13 and 9 times constraint violation for each MG can be avoided in the robust day-ahead scheduling plan. Moreover, the purchased electricity from the grid also has a distinct reduction. It can be seen from the numerical simulation results that one MG can reduce 15.1% and 11.1% of the agreed electricity in the DR intervals. This proposed strategy does not only help MGs decrease the operation cost but also aid utility company relieving the peak demands. • Economy and environment optimal plans for networked micro-grids are proposed. • Stochastic and robust combined method is employed to handle multiple uncertainties. • Demand response considered online optimisation is proposed in intraday stage. [ABSTRACT FROM AUTHOR]