Electricity Distribution Market Research Articles

Design of distributed trading mechanism for prosumers considering the psychological gap effect in community electricity markets

With the large-scale customer-side development of distributed energy and continuous advancements in power market reform, the market-oriented trading of distributed power generation with the participation of prosumers has gradually become an important solution to promote the consumption of distributed energy. In addition, prosumers exhibit obvious bounded rationality characteristics; therefore, the design of efficient and reasonable trading mechanisms determines the efficiency of the market. From this perspective, a distributed electricity market trading mechanism based on the Stackelberg game is proposed for the community electricity trading market, with multiple prosumers participating in the distribution side. Firstly, the trading architecture of the community electricity market was established under the distribution network level. Secondly, the pricing strategy of the distribution network operator and the quantitative strategy of community prosumers were analysed; on this basis, the characteristics of the bounded rationality of prosumers were considered, and the impact of the psychological gap effect of prosumers on their energy utility was introduced into the objective function of prosumers. This facilitated the construction of a Stackelberg game model in which distribution network operators lead and community prosumers follow. Thirdly, the Stackelberg equilibrium was proven to exist and be unique, which was then solved by the distributed algorithm – combining the evolutionary algorithm and quadratic programming – which improves the solution efficiency and effectively protects the privacy of prosumers. Finally, the effectiveness and feasibility of the proposed trading mechanism were verified through numerical examples.

КОНЦЕПЦІЯ ПОБУДОВИ СТРУКТУРНО МІНЛИВОЇ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ УКРАЇНИ

To support the sustainable functioning and development of the energy sector in the conditions of terrorist and military threats, the concept of building a structurally variable electric power system (EPS) is proposed. Structural variability is defined as the ability of the EPS to reproduce such a variety of subsystems and electrical connections between them, which enables the operator to manage the structure of the EPS and, in this way, ensure the stable operation of the electric power industry in conditions of purposeful destructive actions. The concept of a structurally changing EPS is a strategy for anticipating challenges and threats to the sustainable functioning and development of the electric power industry. In order to build a structurally variable EPS, it is proposed to create regional EPSs with their own capacities for production, storage, distribution and supply of electricity in volumes sufficient for consumption by the population, housing and communal services, transport and agriculture within each region. The establishment of the territorial dimensions of individual subsystems, i.e. regional EPS, is determined on the basis of a compromise between the necessary number of such subsystems and connections between them, which ensure the desired degree of variability of the EPS, and the capitalization levels of the regions sufficient to support investment and operating costs for the corresponding regional EPS. It is assumed that the national EPS supports the regional EPS with agreed amounts of maneuvering and reserve capacities and ensures the supply of electricity to enterprises of industrial, construction, transport and other types of economic activity that are of national importance and do not join the regional EPS. In cases of destruction of individual regional power plants, the national power plant, as well as undamaged neighboring regional power plants, together provide the electricity needs of the affected regions. In the structurally variable EPS, uniform rules of behavior of all energy companies as market participants interacting at the national and regional levels should apply. For the organization of such interaction, it is proposed to apply the decomposition of trading platforms and form a distributed electricity market from interconnected upper-level electricity market and regional electricity markets. References 12, figures 6.

A genuine V2V market mechanism aiming for maximum revenue of each EV owner based on non-cooperative game model

As the number of electric vehicles (EVs) surges, EVs are expected to supply auxiliary services for power grid through the vehicle-to-grid (V2G) operation. Specifically, vehicle-to-vehicle (V2V), as a beneficial supplement of V2G, has received increasing attention owing to the capability for flexible EVs deployment and profitable individual interaction. In this paper, a genuine V2V market mechanism aiming for maximum revenue of each EV owner is proposed to construct a distributed electricity market. It reveals the dominance of EV owners from the perspective of market and allows the trade among EV owners in local distribution grid with charging points such as charging stations and auto parks. To this end, the implementation of V2V market mechanism is analyzed in detail, including the electricity price, the process and the integration with the internet of smart charging points (ISCP), a cyber-physical architecture. Then, EVs’ energy dispatching is formulated via the non-cooperative game model, in which each EV owner anticipates maximizing individual revenue by choosing feasible strategies. The existence and uniqueness of Nash Equilibrium (NE) in a non-cooperative game is proved by means of variation inequality (VI) theory. Finally, the ISCP-based optimal algorithm is developed to solve the NE efficiently, thereby maximizing reward of each EV owner. Comprehensive case studies show that the proposed V2V market mechanism can implement distributed dealing among EV owners with reducing individual charging cost and regional load variance effectively. Specially, the costs of EV1, EV8 and EV9 decrease by 31%, 36% and 34%, respectively. And V2V charging demonstrates much lower variance (10.5209) than regular charging (14.1124). Additionally, the convergence of the algorithm is also demonstrated, reaching the NE in less than 30 iterations for each EV.

Using machine learning to select variables in data envelopment analysis: Simulations and application using electricity distribution data

Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.

Nested Bilevel Energy Hub Bidding and Pricing With Price-Responsive Demand

The increasing interdependence among different energy systems has led to the development of the integrated energy system (IES), where the energy hub (EH) is a primary market player in the distribution-level energy market. In an EH, subsystems such as electric, natural gas, and heating systems coexist and are interdependent. This paper proposes an innovative bilevel strategic decision-making framework of an EH that maximizes profit in the energy market and simultaneously reallocates profit among EH subsystems through a dynamic pricing mechanism. In the upper level, the EH acts as a price maker in the day-ahead distribution electricity market for profit maximization. In the lower level, Nash-Bargaining-based profit redistribution is realized through bilateral pricing between energy subsystems in the EH, where the fairness and interests of individual subsystems are respected. The price-responsive load couples the profit maximization and profit reallocation problems. Case studies demonstrate that the proposed framework can effectively solve the bidding and pricing problem of an EH in a day-ahead market, and the cooperation within energy subsystems contributes to higher overall profit and better fairness for the EH subsystems.

Distribution Locational Marginal Pricing Under Uncertainty Considering Coordination of Distribution and Wholesale Markets

A rapidly growing amount of small-scale distributed energy resources (DERs) integrated into distribution systems call for an effective distribution electricity market to manage the uncertainty of DERs and remove barriers to the participation of DERs in wholesale electricity markets. To this end, this paper proposes an uncertainty-aware distribution locational marginal pricing (DLMP) mechanism based on robust optimization for day-ahead distribution markets within a transmission-distribution coordinated framework. The transmission-level model clears the wholesale market and forms transmission locational marginal prices (LMPs) to price energy, reserve, and uncertainty. At the distribution level, a robust optimization-based DLMP mechanism is proposed that internalizes uncertainties and coordinates with the wholesale market. Besides active and reactive power DLMPs, the uncertainty DLMP is introduced to reward reserve and charge uncertainties. The novel DLMP mechanism provides transparent and comprehensive price signals for managing voltage, congestion, loss, especially uncertainty. The coordinated model is solved in a decentralized manner by heterogeneous decomposition algorithm. Distribution and wholesale markets are integrated through the coordinated mechanism to fully utilize generation resources. Accordingly, DMLPs are highly correlated and consistent with transmission LMPs, and thus allow DERs to participate in wholesale markets. The effectiveness of the proposed method is verified via numerous case studies.

Strategic active and reactive power scheduling of integrated community energy systems in day-ahead distribution electricity market

The increasing penetration of distributed energy resources (DERs) enable integrated community energy systems (ICESs) as emerging techno-economic energy entities at the end-user side. In the deregulated electricity market, the ICESs can actively participate in the distribution electricity market (DEM) to provide active and reactive power dispatched by ICES operator (ICESO). In this context, a strategic active and reactive power scheduling model for ICESs in the day-ahead DEM is proposed in this paper. A novel trading mechanism designed for ICESO and distribution system operator (DSO)’s interaction toward both active and reactive power is represented in the day-ahead DEM, where distribution locational marginal prices (DLMPs) for both active and reactive power are introduced as the market settlements. Then, the trading process is formulated as a bi-level programming problem. The upper level describes the combined cooling, heating, and power (CCHP)-dominated ICESO’s strategic active and reactive power dispatch, in which the DLMPs for active and reactive power, internal flexibility services, and optimal dispatch for inverter-based distributed generators are considered. The lower level performs a DEM clearing for both active and reactive power. Further, Karush–Kuhn–Tucker (KKT) conditions, Big-M approach, and duality theory are used to convert the bi-level model from a Mathematical Programing with Equilibrium Constraints (MPEC) model to a single-level mixed-integer second-order cone programming (MISOCP) model for efficient calculation. Finally, case studies on modified IEEE 33-node and IEEE 123-node distribution systems are adopted to evaluate the performance of the proposed scheduling method. The results show that the proposed scheduling approach can contribute to the renewable energy share and energy efficiency, reduce CCHP-dominated ICESs’ operation costs by 38.13% and 13.21% compared to the case where ICESO only acts as a price-taking consumer, and 6.64% and 3.44% compared to the case where ICESO does not consider reactive power bids/offers, respectively.

Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market

In stochastic frontier analysis, the conventional estimation of unit inefficiency is based on the mean/mode of the inefficiency, conditioned on the composite error. It is known that the conditional mean of inefficiency shrinks towards the mean rather than towards the unit inefficiency. In this paper, we analytically prove that the conditional mode cannot accurately estimate unit inefficiency, either. We propose regularized estimators of unit inefficiency that restrict the unit inefficiency estimators to satisfy some a priori assumptions, and derive the closed form regularized conditional mode estimators for the three most commonly used inefficiency densities. Extensive simulations show that, under common empirical situations, e.g., regarding sample size and signal-to-noise ratio, the regularized estimators outperform the conventional (unregularized) estimators when the inefficiency is greater than its mean/mode. Based on real data from the electricity distribution sector in Sweden, we demonstrate that the conventional conditional estimators and our regularized conditional estimators provide substantially different results for highly inefficient companies.

Design of an optimal P2P energy trading market model using bilevel stochastic optimization

In a smart grid, with the emergence of behind-the-meter distributed energy resources (DER) integration, multiple consumers are emerging as prosumers. A prosumer’s chance of maximizing the payoff through peer-to-peer (P2P) trading is bringing a prolific change in the existing energy market. However, for any successful P2P trading, it requires a mutual agreement on participation (peer-matching) between a prosumer (seller) and a consumer (buyer). The presented work projects a two-level stochastic P2P energy trading model between the neighborhood peers in the distributed electricity market (DEM). A systematic and structured peer-matching mechanism, namely ‘enTrade’, is designed and introduced to accelerate the real-time P2P trading between the players (buyer & seller). The algorithm is developed into two parts. In the first part, we consider the players’ stochastic behavior in decision-making, and a joint probability density function is used to design the players ‘stochastic optimal bidding strategy. In the second part, the ‘enTrade’ uses a game theory-based leader–follower model to maximize the likelihood of players ‘bid matching for a successful P2P energy trading in DEM and optimizes the player’s payoff. The developed algorithm uses the transmission constraints to show that the voltage loss between the trading nodes is within the limit. Further, a low-voltage distribution transformer (LVDT) node-27 flow bus is used to showcase the efficacy of the presented work for a successful P2P transaction between the ‘enTrade’ matched-up peers.

Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model

This paper proposes a time-series stochastic socioeconomic model for analyzing the impact of the pandemic on the regulated distribution electricity market. The proposed methodology combines the optimized tariff model (socioeconomic market model) and the random walk concept (risk assessment technique) to ensure robustness/accuracy. The model enables both a past and future analysis of the impact of the pandemic, which is essential to prepare regulatory agencies beforehand and allow enough time for the development of efficient public policies. By applying it to six Brazilian concession areas, results demonstrate that consumers have been/will be heavily affected in general, mainly due to the high electricity tariffs that took place with the pandemic, overcoming the natural trend of the market. In contrast, the model demonstrates that the pandemic did not/will not significantly harm power distribution companies in general, mainly due to the loan granted by the regulator agency, named COVID-account. Socioeconomic welfare losses averaging 500 (MR$/month) are estimated for the equivalent concession area, i.e., the sum of the six analyzed concession areas. Furthermore, this paper proposes a stochastic optimization problem to mitigate the impact of the pandemic on the electricity market over time, considering the interests of consumers, power distribution companies, and the government. Results demonstrate that it is successful as the tariffs provided by the algorithm compensate for the reduction in demand while increasing the socioeconomic welfare of the market.

Transaction search engine of distributed electricity market trading platform based on blockchain technology

AbstractCurrently, it is difficult for China's electricity trading platform to handle large amounts of data during centralized clearing, achieve cross‐regional sharing of transaction information, and realize the optimized allocation of resources. The previous research has proposed establishing a new distributed electricity market trading platform based on blockchain technology to solve the aforementioned problems; however, transaction nodes on the platform have difficulty determining the final delisting plan in the face of numerous choices. Therefore, in this study, a search engine was designed according to the demands of the delisting node. The developed algorithm is run with a programmed script through a smart contract and can provide the best delisting plan for delisting nodes, ensuring that the delisting plan is selected intelligently and publicly. Overall, seven delisting strategies were designed. Additionally, the improved Critic‐G1 method based on the coefficient of variation was used to establish a smart contract for the comprehensive evaluation of listing nodes. Finally, the example analysis verified that calling the smart contract can help the delisting node quickly obtain the best delisting strategy, increase the enthusiasm of the transaction subject, and facilitate the promotion and application of the transaction platform based on blockchain technology, confirming the effectiveness of the proposed method.

Credible Peer-to-Peer Trading with Double-Layer Energy Blockchain Network in Distributed Electricity Markets

Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

Analysis of the impact of COVID-19 pandemic on the Brazilian distribution electricity market based on a socioeconomic regulatory model

This paper evaluates the impact of the COVID-19 pandemic on the Brazilian distribution electricity market by applying a socioeconomic regulatory model called Optimized Tariff Model. The model quantifies the impact of the pandemic on the consumer’s quality of life and also on the economic performance of power distribution companies. Results indicate that both consumers and power distribution companies have been significantly affected in Brazil, especially companies that did not have access to the public policy proposed by the government, as they exhibited economic losses of more than 1 (GR$) in total. After analyzing the impact of the new coronavirus and the actions of the government in this context, an alternative mitigation measure (public policy) is proposed based on the Optimized Tariff Model and its feasibility examined. The alternative mitigation measure is also compared to the public policy proposed by the government and proved to be advantageous in some respects such as making bank loans unnecessary.

Distribution Market-Clearing and Pricing Considering Coordination of DSOs and ISO: An EPEC Approach

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

Trading-oriented battery energy storage planning for distribution market

In this paper, we present a trading-oriented battery energy storage system (BESS) planning model for a distribution market. The proposed planning model is formulated as a mutual-iteration and multi-objective two-stage optimization problem. The first stage is designed to optimize the internal resources allocation including PV system, wind turbine, gas turbine and BESS. Then a double-side auction model named as averaging pricing market (APM) mechanism is presented in the second stage, which aims at solving the issue of energy trading benefit loss caused by the relatively high difference between time-of-use (ToU) electricity price and feed-in-tariff. The contribution of this paper lies in combining the planning of BESS with the optimization of the distribution electricity market. Numerical results on a modified IEEE 24-bus system are presented to demonstrate the superiority of the two-stage optimization model and the sensitivity of the model is also investigated based on regret analysis.

Дослідження практики зниження ризиків міжнародного трансферу технологій в умовах загострення конкурентної боротьби

Analysis of at the global energy market, according to the Advanced Energy Now 2019 Market Report, showed that the global electricity transmission and distribution market demonstrated the highest historical aggregate annual growth rates (CAGRs) between 2011 and 2019. A huge number of small and medium companies got involved into it and into transfer technology process generally.

A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market

With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.

Equilibrium Analysis of Multi‐Energy Markets with Microgrids Bidding

As an effective way to accommodate the distributed renewable energy, microgrid participating in the electricity market competition is an inevitable trend. Because of severe uncertainty and poor dispatchability of the distributed renewable energy in the microgrid, how to counterbalance the deviation between microgrid's actual output/demand and cleared quantity from the market is an important issue needed to address. First, a multi‐energy trading framework is introduced where microgrids bid in a distribution electricity market (DEM). An arbitrageur is presented to buy and sell electricity between the DEM and the day‐ahead wholesale market (DAWM). In order to compensate the bid deviations of microgrids with uncertain output/demand in the DEM, the power to gas (P2G) technologies are applied by a microgrid when it has excess electricity, while the natural gas generating unit (NGGU) is applied when it has deficit electricity. Second, based on the multi‐energy trading framework, a joint stochastic equilibrium model for the DEM, DAWM, and the gas market (GM) is proposed. In addition, Monte Carlo simulation and scenario reduction techniques are applied to tackle the uncertainty in the actual output/demand of microgrids. Finally, the numerical examples show that the proposed multi‐energy trading model can compensate the deviation of the microgrids with uncertain output/demand in the DEM, and help to improve the market competitiveness of microgrids and the utilization of distributed renewable energy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Correction to “A Pool Strategy of Microgrid in Power Distribution Electricity Market” [Jan 20 3-12

Correction to “A Pool Strategy of Microgrid in Power Distribution Electricity Market” [Jan 20 3-12

Power Markets with Information-Aware Self-scheduling Electric Vehicles

We consider multi-period (24-h day-ahead) multi-commodity (energy and regulation reserves) decentralized electricity transmission and distribution (T&D) market designs. Whereas conventional centralized generators with uniform price quantity offers are scheduled by a transmission system operator, low-voltage network-connected distributed energy resources (DERs) with complex preferences and requirements, such as electric vehicles (EVs), are allowed to self-schedule adapting to spatiotemporal marginal cost-based prices. We model the salient characteristics of interconnected T&D networks, and we consider self-scheduling DER responses under alternative distribution network information-aware or information-unaware market designs. Moreover, we consider a single (EV load aggregator) network information-aware scheduler market design. Our contribution is the characterization and comparative analysis—analytic as well as numerical—of equilibria, using game-theoretical approaches to prove existence and uniqueness, and the investigation of the role of information on self-scheduling and EV aggregator coordinated EV scheduling. Finally, we derive conclusions on the impact to social welfare and distributional equity of information-aware/information-unaware self-scheduling as well as single EV aggregator scheduling and implications that are relevant to market design and policy considerations.