Bidding Model Research Articles

Bidding Strategy for the Alliance of Prosumer Aggregators in the Distribution Market

Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the distribution market bidding strategy. Firstly, based on the framework for prosumer aggregator alliances participating in distribution market trading, a bilevel bidding model is constructed. The upper level represents the optimal decision-making model for the prosumer aggregators, while the lower level constitutes the distribution market-clearing model. Secondly, the additional benefits obtained by the alliance are distributed more fairly using the improved Shapley value based on the PV self-consumption rate. Given the problem that the traditional diagonalization algorithm (DA) has an excessive number of iterations when solving the game equilibrium problem of multiple subjects, the DA is improved by optimizing the initial value of the inputs. Finally, case studies are conducted based on the improved IEEE-33 bus distribution system to validate the feasibility and economic viability of the proposed strategy. The case study results show that forming cooperative alliances to participate in market bidding can significantly increase overall profits. The improved DA reduces the number of bids and computation time by 75% and 80%, respectively. Additionally, the improved Shapley value facilitates compensation for some of the aggregators.

A fully decentralized demand response and prosumer peer-to-peer trading for secure and efficient energy management of community microgrid

A novel demand response program combines the advantages of price-based demand response under the time-of-use pricing and the incentive-driven demand reduction bidding strategy to manage energy demand, and maximize the net present value of both participants and the community-microgrid. It also facilitates the seller prosumers to optimally sell the surplus power in a fully decentralized peer-to-peer power trading manner. A novel mathematical demand reduction bidding model is proposed to minimize the community-microgrid cost by the prioritized selection of participants for demand reduction. This study proposed a novel decentralized three non-cooperative dynamic games strategy, incorporating Nash equilibrium approaches to achieve the optimum schedules of appliance, buying, and power trading agents to maximize the net present value of both prosumers and the community-microgrid while preserving participants’ privacy and control network congestion. The priority-based optimal selection of seller and buyer prosumers according to their utility and pricing function in the peer-to-peer power trading scheme is accomplished using a proposed color-based segmented decentralized bilateral power trading to maximize the net present value. The numerical and simulation results demonstrate the feasibility and effectiveness of the proposed strategies and increase the net present value of prosumers up to 42.4 % and community-microgrid up to 34.79 %.

Electric vehicle aggregator as demand dispatch resources: Exploring the impact of real-time market performance on day-ahead market

As the integration of variable renewable energy sources (VREs) into power grids escalates, effectively managing the unpredictability of VRE generation becomes crucial. Electric vehicles, utilized as distributed energy storage, emerge as a potential solution. However, it remains unclear whether electric vehicle aggregators (EVAs), acting as demand dispatch resources, have a favorable impact on the overall electricity market, especially when considering their performance in real-time markets during day-ahead energy allocations. Therefore, this paper proposes a decentralized clearing mechanism (DCM) relying on an multi-attribute evaluation model of EVAs for optimal bidding strategies in the day-ahead market. The EVAs multi-attribute evaluation model employs an integration of the Entropy-Analytic Hierarchy Process, allowing for the simultaneous consideration of real-time performance and day-ahead bidding prices of EVAs. Consequently, the decentralized clearing mechanism supports strategic electricity planning in the day-ahead market by incorporating the actual market performance of EVAs. Furthermore, a real-time market bidding model is developed to balance dynamic mismatches between VRE generation and demand, aiming to maximize profits through optimized real-time commitment and real-time dispatch. System simulations on the IEEE39 bus system demonstrate that the DCM enhances overall profits by 18.69% compared to the centralized clearing mechanism.

A multi‐level coordinated scheduling strategy for shared energy storage systems under electricity spot and ancillary service markets

AbstractThis paper proposes a multi‐level coordinated scheduling strategy for shared energy storage systems (SESS) under electricity spot and ancillary service markets to maximize the overall operational profit. At the upper level, an optimal day‐ahead bidding model is formulated to allocate optimal capacities of SESS for engaging in electricity transactions and offering peak shaving reserve over the scheduling horizon. Then, a joint market clearing model is presented to simultaneously optimize awarded capacities of SESS and marginal prices of electricity spot and ancillary service markets at the middle level. Furthermore, the lower level aims to correct the coordinated scheduling strategy for minimizing the intra‐day scheduling cost under the uncertainty of renewable energy sources and load demand. Finally, a linear relaxation method is applied to transform the proposed strategy into a mixed‐integer linear programming problem for enhancing the computation efficiency. Comparative case studies have validated the superior performance of the proposed strategy for better utilization of available storage capacity and arbitrage maximization.

Demand bidding vs. demand response for industrial electrical loads

We investigate a novel optimal demand bidding model for industrial loads based on an extended optimal power flow problem for the day-ahead market. The objective function accounts for generation costs and revenue resulting from the sale of products made by the bidder using electricity. As a bidding entity, the industrial load participates in electricity price formation, and is described using a model whose parameters can be determined mostly from public information. Constraints specific to industrial loads, such as satisfying product demand, are also included. The model is validated using simulations of a modified IEEE 24-bus reliability test case with a chlor-alkali plant as the load entity. We find that the proposed participation model, which is simple enough to include in grid dispatch software, performs well relative to other demand-side management strategies such as price-based demand response and cooperative scheduling of industrial load by the grid operator.

A two-tier bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism for coupled electricity and carbon markets

To facilitate the coupling of electricity and carbon markets, a two-layer bidding model considering a multi-stage offer‑carbon joint incentive clearing mechanism is proposed. The upper layer models the bidding strategies of renewable energy units and thermal power generating units, and the lower layer is the market clearing layer. A multi-stage offer‑carbon joint incentive clearing mechanism is proposed. In the pre-clearing stage, the environmental benefit factor of the carbon market is introduced to adjust the bidding strategy of the units, the carbon emission intensity enters the market in the form of price, the environmental benefit of the generating units is fully considered, and the carbon quota allocation model of units is established; in the second stage, carbon trading cost is added based on the pre-clearing model to further optimize the unit output; in the third stage, the clearing model with the minimum cost of electricity energy is established for tracking the clearing price. Under this mechanism, the carbon trading cost is traded on the market and unit offers are combined with carbon element to participate in market trading. Three bidding models with different clearing mechanisms are set up, and the results of the arithmetic example show that the proposed model can realize the balance between the economic and low-carbon goals of market clearing, and can achieve the maximum carbon emission reduction based on no increase in the total cost of electric-carbon coupling. As the share of renewable energy increases, the clearing price will decrease. A reasonable offer from renewable energy sources can avoid their interests being jeopardized. Concurrently, increasing the initial carbon price can effectively guide the electricity market to develop in a cleaner direction.

Research on the participation model of energy storage in electricity spot markets considering energy constraints and cost characteristics

In the context of power systems with a high proportion of renewable energy, energy storage plays a significant role in facilitating the consumption of renewable energy and ensuring the operational safety of power systems. However, the current power spot market's predominant power bidding model does not fully consider the physical and cost-operational characteristics of energy storage, which is not conducive to further incentivizing investment and construction of energy storage, and may indirectly affect the flexibility of energy storage in peak shaving and valley filling. This paper summarizes the key issues that need to be addressed for energy storage to participate in the spot market from two aspects: the power bidding model does not meet the requirements of the physical and cost-operational characteristics of energy storage, and the real-time market under this model cannot achieve optimal allocation of energy storage. Considering the energy constraints and cost characteristics of energy storage, a charge and discharge bidding model is proposed, which is based on the stored energy value of energy storage and is in line with the physical and cost-operational characteristics and real-time optimization needs of energy storage. Subsequently, a market clearing model for energy storage participation in the spot market under the state of energy bidding method is constructed, and based on the IEEE 39-bus test case, a comparative analysis of the nodal electricity prices, energy storage revenue, and total system costs under the proposed market participation model and the traditional power bidding model is conducted. Simulation results show that the proposed energy storage participation model in the spot market can better utilize the value of energy storage in peak shaving and valley filling compared to the conventional power bidding model, reducing the extreme electricity prices by up to 10%, increasing single cycle revenue of energy storage by 46%, and reducing the total operating costs of the system in scenarios with significant deviations in system load in the day-ahead and real-time markets.

A Game Bidding Model of Electricity and Hydrogen Sharing System Considering Uncertainty

A Game Bidding Model of Electricity and Hydrogen Sharing System Considering Uncertainty

A sample robust optimal bidding model for a virtual power plant

In many energy markets, the trade amount of electricity must be committed to before the actual supply. This study explores one consecutive operational challenge for a virtual power plant—the optimal bidding for highly uncertain distributed energy resources in a day-ahead electricity market. The optimal bidding problem is formulated as a scenario-based multi-stage stochastic optimization model. However, the scenario-tree approach raises two consequent issues—scenario overfitting and massive computation cost. This study addresses the issues by deploying a sample robust optimization approach with linear decision rules. A tractable robust counterpart is derived from the model where the uncertainty appears in a nonlinear objective and constraints. By applying the decision rules to the balancing policy, the original model can be reduced to a two-stage stochastic mixed-integer programming model and then efficiently solved by adopting a dual decomposition method combined with heuristics. Based on real-world business data, a numerical experiment is conducted with several benchmark models. The results verify the superior performance of our proposed approach based on increased out-of-sample profits and decreased overestimation of in-sample profits.

Trading strategies of energy storage participation in day-ahead joint market based on Stackelberg game

The goal of "carbon peak, carbon neutral" and the increasing expansion of new energy have helped to advance the development of energy storage. However, since the operating cost of energy storage is high, carbon emission trading and power market trading have emerged, effectively improving the efficiency. In this paper, a trading strategy and bidding framework of energy storage participation in the day-ahead joint market are studied. A market bidding model has been established in a framework based on the Stackelberg game. Finally, the "Day-Ahead and Intra-Day and Carbon Emission Trading (CET)" market clearing model has been constructed. It has been simplified to solve the equivalent mixed-integer linear programming (MILP) problem with equilibrium constraints through the use of the Karush–Kuhn–Tucker (KKT) optimality criterion and duality principle. The proposed model is validated through improved examples to obtain thermal unit output cuts of up to 32.2% during load trough periods, and up to 16.75% increase in clearing prices during peak load periods. The storage life is extended and the storage output variation is minimized.

Transferable Energy Storage Bidder

Energy storage resources must consider both price uncertainties and their physical operating characteristics when participating in wholesale electricity markets. This is a challeng-ing problem as electricity prices are highly volatile, and energy storage has efficiency losses, power, and energy constraints. This paper presents a novel, versatile, and transferable approach combining model-based optimization with a convolutional long short-term memory network for energy storage to respond to or bid into wholesale electricity markets. We test our proposed approach using historical prices from New York State, showing it achieves state-of-the-art results, achieving between 70% to near 90% profit ratio compared to perfect foresight cases, in both price response and wholesale market bidding setting with various energy storage durations. We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia. The result shows transfer learning achieves exceptional arbitrage profitability with as little as three days of local training data, demonstrating its significant advantage over training from scratch in scenarios with very limited data availability.

Intelligent strategic bidding in competitive electricity markets using multi-agent simulation and deep reinforcement learning

Aiming at the lack of comprehension of agents in Multi-Agent Simulation (MAS) based on classic Reinforcement Learning algorithms of competitive electricity markets, an intelligent strategic bidding method using Deep Reinforcement Learning (DRL) and MAS is proposed in this paper, which not only can provide more intelligent strategies for market participants to maximize their profits, but can enhance the performance of simulation models dealing with high-dimensional continuous data in electricity markets. Firstly, a theoretical framework of intelligent strategic bidding in competitive electricity markets based on MAS and DRL is proposed, and the process of intelligent bidding in electricity markets based on MAS and DRL is described. Then, three MAS models of intelligent strategic bidding are built based on three classic DRL algorithms, including Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Asynchronous n-step Q-learning (Async n-step QL), and three algorithms’ convergence speed, computational efficiency, and response sensitivity are compared and analyzed. Finally, a novel Improved Async n-step QL (IAsync n-step QL) algorithm is proposed, the MAS model based on the IAsync n-step QL algorithm for intelligent strategic bidding is established. Simulation results show that the model using the novel DRL algorithm is more profitable and responsive than the classic DRL algorithms.

Intra-provincial Medium- and Long-term Bidding Model Incorporating Inter-provincial Markets and Available Transmission Capacity

Intra-provincial Medium- and Long-term Bidding Model Incorporating Inter-provincial Markets and Available Transmission Capacity

A Fast Polytope-Based Approach for Aggregating Large-Scale Electric Vehicles in the Joint Market Under Uncertainty

The aggregation of electric vehicles (EVs) has been advocated as an effective means to manage and control large-scale plug-in EVs. However, the efficient aggregation is challenging due to the heterogeneity and uncertainty of individual EVs. To cope with this challenge, this paper puts forth a fast polytope-based aggregation approach, which could deal with the time-coupled, uncertain, and heterogeneous individual EV models. First, the feasible region of each EVs power profiles is expressed as a polytope under H-representation considering various operational constraints. To improve the aggregation efficiency, H-representation reconstruction and polytopic projection processes are designed to address the heterogeneity of EVs. The uncertainties of EVs are modeled by the distributionally robust joint chance-constrained programming. The original individual and aggregate feasible region of EVs are subsequently inner approximated by the homothets of a basic prototype. Last, a day-ahead optimal bidding model for charging service provider in a joint market, including energy, peak-shaving and reserve markets, is developed. Numerical simulation results based on real-world dataset demonstrate that the proposed aggregation method can improve both the computation efficiency and accuracy of approximate feasible region.

Research on Bidding Model of Electricity Selling Company Considering Green Certificate Trading and Electricity Selling Blocking

This article considers the optimal bidding model of traditional thermal power generation companies’ electricity sales companies, taking into account transmission congestion, under the influence of new policies such as the renewable portfolio standard (RPS) and the renewable exchange certificate (REC). Firstly, the transaction cost of green certificates is included in the cost of electricity sales, and an optimization model is established with the goal of maximizing the profit of the electricity sales company; Secondly, the node electricity price method is used to manage transmission congestion, and an optimization model is established with the goal of minimizing the cost of transmission congestion; Finally, multi agent technology and dynamic game theory are used to solve the optimal bidding strategy of the electricity selling company.

OPTIMIZATION OF THE AUCTION DURATION IN THE PRESENSE OF TIME-DEPENDANT COSTS

This paper examines the influence of the duration of auctions or tenders on the expected gain of their organizer. Extending the duration of bidding affects auction results in two ways. On the one hand, it allows attracting a larger number of participants to the auction, and the competition between them increases the chances of the auctioneer to get a better price. On the other hand, delaying bids delays the receipt of money (for auctions) or required goods or services (for tenders), and time has value in itself. The influence of these two factors, which act in opposite directions, suggests the existence of an optimal duration of the bidding process. The paper develops a mathematical model of bidding, which formalizes these considerations and provides an algorithm to determine their optimal duration. The arrival of bidders willing to participate in the auction is modeled as a Poisson process. Each participant is characterized by his own assessment of the value of the object put up for auction. These estimates are assumed to be independent identically distributed random variables drawn from some parametric distribution. Under these assumptions, Myerson's revenue equivalence theorem makes it possible to predict the expected results of the auction as a function of the number of bidders, regardless of the auction rules. On this basis, it is possible to compare the benefits and costs associated with changing the duration of time for accepting applications for participation in bidding, which makes it possible to determine its optimal value. The obtained optimality conditions have a meaningful and intuitive economic interpretation. For practical applications, the use of Monte Carlo methods based on the empirical distribution of bid and ask prices is proposed. The practical implementation of the proposed algorithm can improve the economic performance of the auctioneer, which is especially relevant for the public sector of the economy.

Co-Optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-Time Frequency Regulation Service

The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator’s bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.

A risk-averse day-ahead bidding strategy of transactive energy sharing microgrids with data-driven chance constraints

The rapid development of microgrids (MGs) with various prosumers promotes the accommodation of renewable distributed generation (RDG) and provides platforms for local energy sharing among prosumers. However, the operational uncertainties pose enormous challenges to the day-ahead bidding of MGs in the wholesale electricity market and there is an urgent need for a local market to facilitate the local energy sharing. Thus, this paper proposes a risk-averse day-ahead bidding strategy for MGs with full consideration of the multiple uncertainties originating from the wholesale electricity market, RDG and loads. Based on the transactive energy (TE) sharing concept, the local market is formulated as a Stackelberg game (SG) to effectively capture the strategic interaction among the MG and prosumers, where a distributed iterative algorithm with a bisection approach that only requires exchanging TE-related information is adopted to achieve the SG equilibrium without compromising privacy concerns. To handle the uncertainties of RDG and loads, the power balances are formulated as chance constraints and a data-driven quantile forecasting method is developed for achieving the computational tractability of chance constraints without any prior knowledge or probability distribution assumptions. Furthermore, a risk criterion of the conditional value-at-risk is incorporated in the day-ahead bidding model of MGs for risk aversion towards uncertainties of the wholesale electricity market. The effectiveness of the proposed solution is extensively demonstrated through numerical simulation.

Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach

The high-renewable-penetrated power system frequently requires frequency regulation services. By aggregating heterogeneous demand-side flexible resources, the virtual power plants (VPP) are able to quickly respond to the frequency regulation signal, enabling them promising frequency regulation service providers. However, due to the difference in ownership, it is unlikely that the self-interested demand-side resources are willing to follow the VPP-oriented dispatch results. To bridge this gap, this paper proposes a decentralized game-based mechanism for the frequency regulation service provision inside the VPP. Firstly, the supply function-based bidding models of aggregated demand-side resources, namely, demand-side flexible resources and renewable distributed generations, are established. Then, a non-cooperative generalized Nash game for frequency regulation service provision is formulated. The existence, uniqueness, and competitive efficiency loss of the game are analyzed by designing an equivalent best response potential game of the original game. Additionally, a decentralized look-ahead real-time market clearing mechanism for frequency regulation service provision inside the VPP is proposed based on dual decomposition algorithm. Finally, the effectiveness and computation efficiency of the proposed method are validated in the case studies. Meanwhile, it is shown that the competition efficiency loss can be effectively reduced from around 80% to 6% when increasing the demand-side resource population from 20 to 2000 as well as balancing the demand-side resource composition.

Receding horizon dispatch of multi-period look-ahead market for energy storage integration

Although noteworthy efforts towards energy storage participation in electricity markets have been witnessed worldwide during the last decades, market mechanisms for optimal storage integration are yet to be comprehensively studied. This paper presents a novel approach wherein a multi-period look-ahead market is proposed to progressively solve the optimisation problem over a receding horizon, incorporating intertemporal constraints to enable forward planning. The focus is specifically on modelling the bidding behaviour of energy storage, and to address this, we develop two distinct models for the participation of energy storage in the market. The integrated model aims to schedule the future dispatch of the storage to minimise total operational cost, taking into account the fixed costs of the storage, while the strategic model pursues the dual ambition to maximise storage profit by formulating a bilevel optimisation problem. We validate the viability of our proposed market mechanisms through case studies on the Australian National Electricity Market. We conduct case studies which provide evidence that both the strategic and integrated bidding models effectively address the requirement for forward planning, resulting in reduced market costs and volatility. Notably, the strategic model outperforms in terms of generating higher storage revenue, while the integrated model exhibits a slight advantage in terms of lower market costs compared to the strategic model.