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Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales. • A hierarchical market framework is designed comprising a hybrid LEM and an LFM. • The hybrid LEM combines a P2P market and community-based markets with VPPs. • The P2P market and community-based markets are coordinated by a modified MCBD. • The LFM is formulated as an MI-SOCP problem, with VPPs providing flexibility service. • VPPs bid block orders so that the operational characteristics of DERs are captured. [ABSTRACT FROM AUTHOR]

Due to the rise of distributed energy resources (DERs), virtual power plants (VPPs) have gained intensive academic and industrial attention. While current centralized VPPs benefit from their large aggregated capacity in the wholesale market, they lack effective incentivizing participants for asset investment and market participation. Conversely, the peer-to-peer (P2P) market offers better profits for prosumers through direct trading; however, transactions hinge on approval from associated authorities, such as electricity retailers, who read their power meters. Such an approval procedure becomes complex, even impossible, when more retailers are involved. Hosting the P2P market in a VPP environment can conveniently resolve this issue. This paper proposes a novel holistic P2P (H-P2P) market model hosted in a VPP environment to provide the VPP participants with financial incentives and optimize the network and market operations simultaneously. The proposed new market structure and trade principle allow the proposed H-P2P market to trade active and reactive energies for both financial benefits and network constraints. Using the novel transactive voltage/VAR control (TVVC) method proposed in this paper, the reactive energy trading through the P2P market can support effective voltage regulation and incentivize the VPP participants to provide such voltage control ancillary services actively. Finally, a novel multi-stage P2P negotiation process is introduced to divide the VPP regulatory, active and reactive energy convergence stages, which allows faster P2P decision-making with improved computing efficiency than the conventional multi-period alternating direction method of multipliers (ADMM). The case study validates that the proposed H-P2P market can increase the financial incentives of the prosumers who install PVs and batteries by a large extent and the pure load units by a relatively smaller percentage. [Display omitted] • A novel synergistic model integrating a holistic P2P market in an internal centralized VPP environment. • Simultaneous active and reactive energy trading in the holistic P2P market regulated by the VPP coordinator. • A novel transactive voltage/VAR control concept with P2P reactive energy trading for the internal VPP voltage control. • An innovative multi-stage P2P negotiation mechanism to converge the active and reactive energy transaction decisions. [ABSTRACT FROM AUTHOR]

• A tri-layer multi-energy market framework is proposed. • Integrated demand response model is set up for load serving entity. • Detailed modeling for an integrated energy system is studied. • A novel conditional value at risk approach is proposed. The rapid development of an integrated energy system makes it difficult for traditional power market to adapt to the trend of multi-energy interactions. Therefore, a tri-layer multi-energy day-ahead market structure and operation mechanism, allowing the simultaneous trading of electricity, heat and natural gas, are proposed in this paper. Concentrating on the profit of the load serving entity in this market, the optimal transaction strategy based on the integrated demand response is explicitly modeled in detail. In particular, the physical constraints of the power distribution network, natural gas network and district heating network are strictly considered. To address the nonlinear and nonconvex problems in the distribution network and natural gas network, the mixed-integer second-order cone programming method and piecewise linearization process are used. Furthermore, a novel conditional value at risk approach is proposed to address the uncertain forecasted market prices, so that the risk can be mitigated. Compared with the traditional electricity market, the LSE can earn a higher profit in the proposed market, and the integrated demand response program enhances the potential of multi-energy peak load shifting. Finally, the effectiveness of the proposed method has been verified on an integrated energy system with IEEE 33-bus power system, an 11-node gas system and a 6-node heat system. A set of comparative cases verify the necessity for the IES to keep the balance between the market economy and network security operation. [ABSTRACT FROM AUTHOR]

Regulators need to pay attention to the negative effects of the transaction costs when they define the reasonable coverage of the emissions trading scheme (ETS). In addition, the market structure in the ETS also needs to be considered in the market efficiency evaluation, as most covered firms come from industries with high market concentration. This paper incorporates transaction costs (monitoring, reporting and verification (MRV) costs and trading costs) and market structure into a partial equilibrium model to study their effect on the reasonable coverage of the ETS. A database of the 1867 industrial firms included in the ETS pilots in China is established for the case study. It is found that the MRV costs become the main factor of the breakdown in efficiency of the ETS. However, there seems to be no inherent relationship between the market structure and the efficient coverage of the allowance market. The policy implications derived from the case study can provide useful references for the upcoming national ETS in China. [ABSTRACT FROM AUTHOR]

• We used EMIS-AS model to explore various market structures and clean energy targets. • Energy-only markets can achieve same clean energy goals as capacity markets. • ORDC scarcity pricing exhibits substitutional relationship with capacity markets. • Even one well-designed market mechanism can achieve desired clean energy targets. • Capacity/reserve eligibility rules for one technology or product can impact others. Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite – Agent-based Simulation (EMIS-AS) model. We apply clean energy targets of 45%–100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the "right" market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non-incremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as capacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve-based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in capacity and operating reserve markets can impact deployment and operations, even for nonrestricted technologies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities. [ABSTRACT FROM AUTHOR]

• A distributed operation optimization model of a large-scale distribution network with peer-to-peer (P2P) electricity trading is proposed. • A network partitioning method based on dynamic reconfiguration in the distributed manner is studied to improve the computation efficiency. • A proxy entity for distribution network is addressed to help the coordination between the electricity trading and the operation of ADNs. • The network usage price can be adjusted to reach the balance between P2P electricity trading maximization and distribution network security. With the increasing integration of distributed generators and the rapid development of electricity markets, active distribution networks (ADNs) confront new challenges in operation optimization. Centralized optimization methods usually aggravate the computational burden when applied to large-scale distribution networks (DNs). A distributed operation optimization model incorporating peer-to-peer (P2P) electricity trading in a blockchain environment is proposed in this paper, where network usage fees considering electrical distance are specially addressed. Meanwhile, the private information security of prosumers is ensured by the Proof-of-Authority (PoA) consensus blockchain during P2P electricity trading. The trading information of adjustable prosumers should be sent to the proxy entity authorized by the DN using the blockchain platform. In addition, a network partitioning method via dynamic reconfiguration considering multi-level switching is offered to adapt to the required distributed manner. The operation network, as well as the corresponding market structure can be automatically updated according to the results of dynamic reconfiguration. Moreover, the unit price of network usage fees can be adjusted to derive a higher amount of P2P electricity trading with security constraints. Case studies on a practical 148-bus system and a 297-bus system verify that the proposed model maximizes the profits and reduces the computational time in the distributed operation optimization. [ABSTRACT FROM AUTHOR]

• A tri-layer ancillary service market mechanism is presented for demand flexibility. • A two-loop Stackelberg game depicts the interactions between the market subjects. • Multi-type ancillary services are considered in the game model. • The two-loop Stackelberg game equilibrium is proved. • A distributed iterative interaction method is presented to clear the multi-type ancillary service market. In order to utilize demand-side flexibility and meet system requirements, the aggregator (AGG) has become a vital role in the ancillary service market. To reflect the willingness of each market subject, a tri-layer ancillary service market framework for ancillary service market operator (ASMO), AGG, and users is proposed in this paper, which contains the interactions between different layers including ASMO and AGG, AGG and users. We develop a detailed market timeline that takes multi-type ancillary services into account. Moreover, due to the hierarchical market structure, we conduct a two-loop Stackelberg game to depict the effect of each market subject's strategic behavior. Based on the game models of all the market subjects, the existence and uniqueness of the game equilibrium point are proved. In addition, considering the correlation between multi-type ancillary services, the clearing process is decomposed into two sub-problems and a distributed joint market clearing based on the particle swarm optimization algorithm is presented. Case study shows that the proposed method is profitable for all the market subjects, the market timeline is reasonable, and the joint clearing method is effective. [ABSTRACT FROM AUTHOR]

• A large-scale active distribution network operation management model with peer-to-peer (P2P) trading is proposed. • Network usage fees considering electrical distance are specially addressed. • The trading agent is introduced to manage the prosumers' trading to simplify the P2P market management. • This operation management model is easy to adapt to the active distribution network reconstruction. With the steady development of electricity trading market, active distribution networks (ADNs) need to face new challenges in operation management. Meanwhile, the traditional operation management with peer-to-peer (P2P) trading leads to a heavy computational burden due to a great number of prosumers. An operation management model for large-scale distribution networks integrated with P2P trading is proposed in this paper, where network usage fees are specially addressed. To simplify the management of the P2P trading market, the trading agent is introduced into the P2P trading platform. In addition, this model is adaptive to the reconfiguration of ADN, so the operation management system and the corresponding market structure can be automatically updated according to the new network topology. As shown in case studies, the total trading amount in the P2P trading market can increase by about 62MWh by adjusting the unit price of network usage fees, and the computational time can decline by about 77% in a practical-scale 152-bus system. Also, the total trading amount can be doubled approximately, and the computational time can be reduced by about 85% in the IEEE 8500-bus system. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increase of Renewable Energy Sources (RES) in energy markets that has to lead their agents to become more proactive. In this scenario, a market structure based on Peer-to-Peer (P2P) transactions is very promising but presents challenges for the network operation. A critical challenge is to ensure that network constraints are not violated due to energy trades between peers and neither due to the use of reserve capacity. In this paper, it is proposed a new iterative sequential approach for energy and reserve P2P market that ensures the feasibility of both energy and reserve transactions under network constraints. The methodology considers the interaction between the prosumers and the Distribution System Operator (DSO) in making the final market/operation decision and can be integrated into the existing distribution system. The proposed approach includes the estimation of reserve requirements based on the RES uncertain behavior from historical generation data, which allows identifying RES patterns. The proposed model is assessed through a case study that uses a 14-bus system, under the technical and economic criteria. The results show that the approach can ensure a feasible network operation encompassing energy and reserve markets. • An optimization approach to integrate prosumer and DSO for energy and reserve market solution. • Inclusion of electrical network constraints into the energy and reserve market problem. • Obtaining feasible results under both the energy and electrical network constraints. • Modeling uncertainties over distributed renewable energy sources through historical generation data. • Evaluation of the reserve calculation from two probabilistic algorithms. [ABSTRACT FROM AUTHOR]

The ubiquitousness of the low cost Information & Communication Technologies (ICT) provides strategic opportunities for the "prosumers", customers that both consume and produce energy, to have a more proactive role in system operation. From an operational perspective, though, this creates additional challenges in managing power distribution networks due to such active grid-edge systems with limited to no visibility or control by the utilities. Transactive Energy (TE) has been emerging as a key enabler towards efficiently coordinating prosumers through market based constructs thereby encouraging more consumer-centric economies. However, communication-based controls are susceptible to losses in information or message delays, potentially de-synchronizing solutions. This work presents a transactive implementation of a community-based electricity market design based on a multi-settlement decentralized coordination strategy. The coordination mechanism presents an algorithm that provides robustness to communication delays. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models and delay-prone communication systems. Case studies on the IEEE-123 node test system demonstrate the feasibility of the proposed community-based TE architecture to collaboratively operate prosumer-communities as grid-edge systems and quantifies their potential benefits. • A decentralized transactive architecture is proposed for community-centric markets. • A multi-settlement (DA & RT) market is designed for optimal coordination. • The proposed market in implemented through a high-fidelity co-simulation framework. • Impacts of non-ideal communication system on the market mechanism is demonstrated. • A methodology has been developed for ensuring robustness to communication delays. [ABSTRACT FROM AUTHOR]

• Two models for the participation of PV power producers in three market floors. • Rescheduling following the characteristic of the six intraday market sessions. • Risk-hedging map associated with the scheduling and rescheduling of the bids. Optimal bidding that considers different electricity market floors can increase the financial gains of photovoltaic (PV) power producers. However, the current approach to trading PV power essentially consists of committing to sell the forecasted PV generation. To analyze profits and investigate new business opportunities for PV power producers, this paper proposes two novel stochastic programming-based methods for scheduling and rescheduling for trading the PV generated energy in day-ahead and intraday electricity markets. Risk-hedging is also considered in terms of co-optimizing the expected profit with the Conditional Value-at-Risk (CVaR) metric. As a consequence of the structure and organization of the market floors and due to different market windows, rescheduling is necessary to exploit the most recent information. Updated rescheduling progressively reveals actual profits or losses, risk-hedging possible engagement in business transactions, and the final effect of strategic bidding. A case study in the Spanish electricity market based on actual data is presented. The analysis of the case study shows the influence of the three market floors (day-ahead, intraday, and imbalance), the participation in multiple intraday sessions, risk-hedging, and rescheduling on the profits of the PV producer. [ABSTRACT FROM AUTHOR]

• P2P electricity trading price and strategy is proposed for prosumers and consumers. • The minimum and maximum electricity trading prices are calculated in South Korea. • The P2P trading prices were calculated based on the market participation conditions. • A maximum of US$0.27/kWh profit can be obtained through P2P electricity trading. • Prosumers and consumers can find optimal P2P trading partners for maximum profits. A successful Peer-to-Peer (P2P) electricity trading within a microgrid requires a P2P electricity trading price and strategy that enable both energy prosumers and consumers to obtain profits. Therefore, this study aims to propose a P2P electricity trading strategy based on the minimum and maximum electricity trading prices for energy prosumers and consumers that ensure their profitability, by considering the actual electricity market structure in South Korea. Towards this end, the minimum and maximum electricity trading prices for energy prosumers and consumers were calculated based on the market participation conditions and electricity trading scenarios established in this study. By matching energy prosumers and consumers based on the calculated minimum and maximum electricity trading prices, a P2P electricity trading strategy was ultimately proposed. As a result, the minimum (i.e., US$0.05–0.34/kWh) and maximum (i.e., US$0.09–0.32/kWh) electricity trading prices increased as the monthly electricity consumption of energy prosumers and consumers increased and the self-consumption and electricity purchase rates decreased. Consequently, the profitable electricity trading scenarios increased as the monthly electricity consumption was lower and the self-consumption rate was higher for energy prosumers, and as the monthly electricity consumption was higher and the electricity purchase rate was lower for energy consumers. In particular, the P2P electricity trading can provide maximum profits to energy prosumers and consumers when the monthly electricity consumption of energy prosumers is 200 kWh, the monthly electricity consumption of energy consumers is 500 kWh, and the electricity purchase rate is 20%. Based on the findings of this study, it is possible not only to determine profitable P2P electricity trading prices to market participants but also to establish an optimal P2P electricity trading strategy by matching energy prosumers and consumers that can ensure them with maximum profits. [ABSTRACT FROM AUTHOR]

• A non-cooperative game model with an embedded notion of each player's reputation. • Market Reputation Index is introduced to ensure fairness and improve reliability. • Model-free approach of extremum seeking is shown to solve for the Nash equilibrium. A comprehensive energy trading market is proposed at the distribution level using a model-free, game theoretic approach. The proposed market is modeled as a non-cooperative, multiplayer game, and a Nash equilibrium solution is obtained using an extremum seeking algorithm. For the game model, a non-cooperative market structure is proposed with an embedded notion of each player's reputation. Besides that, an index is proposed that tracks the commitments of each player, rewards them according to their past behavior, and improves market reliability. Moreover, the proposed index, referred to as a Market Reputation Index, promotes fairness by rewarding good players and also encourages installation of an energy management system with accurate forecasting. A new, low-complexity, model free approach (i.e., Extremum seeking) is demonstrated to model Nash seeking behavior of players and a unique Nash equilibrium solution is sought for the proposed game. Detailed case studies demonstrate how the game is setup and the convergence to Nash equilibrium is achieved. Results are analyzed to show the usefulness of the market reputation index in improving reliability and fairness. It is also shown that the proposed market results in an increased local generation, higher payoffs (profits) for the participating players, and lower market clearing prices. [ABSTRACT FROM AUTHOR]

• An equilibrium problem integrated with individual risk-averse features is proposed. • The conditional value-at-risk method is used for individual risk-averse evaluation. • The linearization and binary expansion method are used to solve the nonlinearity. • The model can better formulate the market with several risk-averse oligopolies. With the deregulation of world power markets, the importance of power market equilibrium analysis tools becomes more prominent. Due to the increasing uncertainty in the power markets, the influences of risk attitudes on the participants' strategic bidding behaviors and market equilibrium become more significant, which makes risk feature formulation more necessary in the market bidding decision-making process. This paper fully considers the risk-averse features of power generation companies based on the concept of the conditional value at risk and proposes a bilevel equilibrium model to practically simulate market behaviors. The mathematical model is essentially a modified equilibrium problem with equilibrium constraints. The upper-level problem of the model maximizes the profits minus the risks of each generation company, while the lower-level problem represents the market clearing processes of day-ahead markets by the independent system operator. A linearization method is proposed to transform the proposed model into a mixed-integer linear model. An illustrative example and a numerical example are performed to show the effectiveness, validity and adaptability of the proposed model. The comparisons with other equilibrium models demonstrate the better performances of the proposed method to simulate the market equilibrium with several oligopolies. The sensitivity analysis is proposed to analyze the phenomenon where different risk-averse levels and market ownership structures would result in different market equilibrium. [ABSTRACT FROM AUTHOR]