Bidding In Electricity Research Articles

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Forecasting individual bids in real electricity markets through machine learning framework

With the increasing uncertainty caused by the complexity of the world’s energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated.

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.

Strategic bidding of gas-fired units with tanks in coupled electricity and natural gas markets

With the increasing interdependence between the electricity and natural gas systems in economy and physics, this paper addresses the problem of developing optimal offering strategies in synchronized electricity–gas markets with step-wise energy offer formats. The gas-fired generators act as price makers in electricity markets and interruptible loads in the natural gas markets. The proposed model considers not only the impact of electricity clearing prices on gas bidding behaviors but also the influence of fuel cost variation on developing optimal electricity bidding curves. A price update method is proposed with the help of gas tanks to suppress price fluctuation. A two-stage trading mechanism is designed for gas-fired generators to optimize bids in electricity and gas markets via an iterative method. These bilinear terms in objectives and constraints are tackled by primal–dual equality conditions and binary expansion method. Then, the mathematical program with equilibrium constraints is converted to the mixed-integer linear program. The diagonalization algorithm is nested in the proposed trading mechanism if there are many leaders in the market instead of just one. Case studies validate the proposed methodology in detail.

Research on multi-microgrid power transaction process based on blockchain Technology

As a new distributed generation system, multi-microgrid is an important part of Energy Internet. There are a series of problems in the selection of generation resources and power trading which includes obvious concentration, fairness and low credibility. Aiming at the shortcomings of microgrid in real-time electricity trade, a multi-microgrid electricity bidding trading model based on blockchain technology is proposed. A competitive spot market with distributed "multi-seller and multi-buyer" is constructed to realize the integration of "source-sale". This paper compares the traditional trading mode with the power trading mode supported by blockchain technology. In addition, this paper establishes a blockchain-based multi-microgrid power trading model, and uses ant colony algorithm random bidding matching to achieve multi-objective optimization. Through the whole process, the unification of energy flow, information flow and currency flow are realized. Finally, the reasonable allocation of power resources and the openness and transparency of power trading are ensured.

Bidding strategy for wireless charging roads with energy storage in real-time electricity markets

The combination of wireless charging roads and energy storage systems is a promising option for electric vehicle charging because of their capabilities in mitigating range anxiety of electric vehicle drivers. Wireless charging road operators can purchase electric energy by submitting price-sensitive demand bids in real-time electricity markets. Efficient bidding strategies are crucial to minimizing the energy costs for providing wireless charging services. In this study, we first propose a composite statistical model based on graph signal processing and linear regression to forecast the future locational marginal prices (LMPs) in a power network. Then an estimate of future electric load on each wireless charging road is derived by simulating its traffic flow using a point queue-based traffic flow model. An efficient price-sensitive bidding strategy for each individual wireless charging road is developed based on its LMP forecast, wireless charging load estimate, and a model predictive control framework. Our numerical example shows that the proposed price-sensitive demand bidding strategy reduces the electric energy cost for operating a wireless charging road with an energy storage system by 6% compared to a baseline bidding strategy.

Impact of the splitting of the German–Austrian electricity bidding zone on investment in a grid-scale battery energy storage system deployed for price arbitrage with gray and green power in Austrian and German day-ahead power markets

The splitting of the German–Austrian electricity bidding zone in October 2018 established limits for cross-border exchanges of power between Germany and Austria, which led to the decoupling of German and Austrian wholesale power prices. We analyze the impact of the split on the investment in lithium-ion grid-scale battery energy storage systems (BESS) used for intertemporal arbitrage in German and Austrian day-ahead power markets with hourly contracts operated by the Energy Exchange of Austria. The paper was written in response to the increasing share of green power in the national energy mix of the two countries, followed by a similar development in the volume of green power traded. Arbitrage with green power was analyzed and compared to arbitrage with power from conventional sources using a robust mixed-integer linear programming (MILP) model extended with a battery degradation process. The model was solved for different levels of depth of discharge (DOD) to identify the optimum between cycle life and calendar aging with respect to prevailing market conditions. Using predicted costs of battery, a break-even point was reached with 60% DOD identified as the optimal pattern for the dispatch of the BESS. A separate scenario was considered for the analysis of the impact of moving from the perfect knowledge of future prices to a situation where day-ahead prices are forecasted using the seasonal autoregressive integrated moving average (SARIMA) model.

A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets

Market agents with renewable resources face amplified uncertainty when forecasting energy production to securely place bids in electricity markets. To deal with uncertainties, stochastic modelling has been applied to optimize the bidding strategy of these market agents. However, studies found in the literature usually focus on day-ahead and balancing markets, leaving aside intraday markets that could be used to correct bidding positions as uncertainty gets resolved. This paper proposes a multistage stochastic decision-aid algorithm based on linear programming to optimize the bidding strategy of market agents in three different electricity markets - day-ahead, intraday, and balance markets. The market agent represents a Virtual Power Plant with wind, solar PV, and storage technologies, and its participation in three electricity markets was compared to the participation in DA and BM markets only. Results show that participating in all three markets increased the profit achieved by the VPP agent by 10.1% while also decreasing the incurred imbalances by 63.8%. The results demonstrate that having accurate tools to deal with the multi-settlement framework of electricity markets while considering the uncertainties of daily operations is key to a successful integration of renewable energy resources into electricity markets and power systems.

Strategic bidding in electricity markets with convexified AC market-clearing process

This paper presents a framework to solve the strategic bidding problem of participants in an electricity market cleared by employing the full AC Optimal Power Flow (ACOPF) problem formulation. Traditionally, the Independent System Operators (ISOs) leveraged DC Optimal Power Flow (DCOPF) problem formulation to settle the electricity market. The main quest of this work is to find what would be the challenges and opportunities if ISOs leveraged the full ACOPF as the Market-Clearing Problem (MCP)? This paper presents tractable mathematical programming with equilibrium constraints for the convexified AC market-clearing problem. Market participants maximize their profit via strategic bidding while considering the reactive power dispatch of generation units. The equilibrium constraints are procured by presenting the dual form of the relaxed ACOPF problem. The strategic bidding problem with an ACOPF-based MCP improves the exactness of the Location Marginal Prices (LMPs) and profit of market participants compared to the one with DCOPF. It is shown that the strategic bidding problem with DCOFP-based MCP is unable to model the limitation of reactive power support. The presented results display cases where the proposed strategic bidding method renders 52.3% more profit for the Generation Company (GENCO) than the DCOPF-based MCP model. The proposed strategic bidding framework also addresses the challenges of coupling real and reactive power dispatch of generation constraints, ramping constraints, demand response implications with curtailable and time shiftable loads, and AC line flow constraints. Therefore, the presented method will help market participants leverage the more accurate ACOPF model in the strategic bidding problem.

Machine Learning-Driven Virtual Bidding With Electricity Market Efficiency Analysis

This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity. The algorithmic trading strategy is developed from the perspective of a proprietary trading firm to maximize profit. A recurrent neural network-based Locational Marginal Price (LMP) spread forecast model is developed by leveraging the inter-hour dependencies of the market clearing algorithm. The LMP spread sensitivity with respect to net virtual bids is modeled as a monotonic function with the proposed constrained gradient boosting tree. We leverage the proposed algorithmic virtual bid trading strategy to evaluate both the profitability of the virtual bid portfolio and the efficiency of U.S. wholesale electricity markets. The comprehensive empirical analysis on PJM, ISO-NE, and CAISO indicates that the proposed virtual bid portfolio optimization strategy considering the price sensitivity explicitly outperforms the one that neglects the price sensitivity. The Sharpe ratio of virtual bid portfolios for all three electricity markets are much higher than that of the S&P 500 index. It was also shown that the efficiency of CAISO's two-settlement system is lower than that of PJM and ISO-NE.

Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model

As one of the important measures for China's power system reform, the electricity bidding pricing (EBP) is helpful to realize the effective allocation of power resources. Based on price conduction theory and complex network modeling technology, we construct the price transmission network for the 76 economic sectors in China, identify the critical path of electricity price transmission, and empirically simulate and analyze the economic impact of EBP in different scenarios from the two aspects of whether the CEPL mechanism is implemented or not. The results indicate that the electricity price will be decreased directly by the EBP, and the electricity industry will significantly reduce the impact on other related industries. Particularly, affected by regulatory policies such as electricity price cap, the electricity price caused by EBP cannot be effectively transmitted to the upstream industries. Secondly, with the simultaneous implementation of the coal-electricity price linkage (CEPL) and the EBP, the coal-electricity price transmission will change from one-way conduction style to two-way interaction style, and the impact of electricity price fluctuations on its related industries will be more significant, while the comprehensive impact of the coal industry on its associated industries will be significantly reduced. Thirdly, there are obvious scenario differences in the impact of different intensity of EBP on the macro-economy. The results show that under the mechanism of CEPL, the EBP with 20% of the total power used for bidding pilot has minimal impact on the Chinese economy. Based on the above conclusions, we propose that China should scientifically determine the scale and the pilot regions of electricity bidding under the CEPL, develop more diversified bidding forms and improve more robust supervision system.

Bidding in local electricity markets with cascading wholesale market integration

Local electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.

A weighted graph‐based method for detection of data integrity attacks in electricity markets

The negative impacts of data integrity attacks against multi-settlement electricity markets have been recently studied. It has been shown that adversaries could launch profitable cyber attacks by casting an incorrect image of transmission lines' congestion pattern while simultaneously submitting bogus bids in electricity markets. Although a variety of countermeasures have been proposed in the literature to detect such data integrity attacks, none of those approaches has been designed specifically to ensure data integrity of multi-settlement electricity markets. The existing methods may even fail to detect complicated types of attacks (e.g. unobservable false data injection attacks) or fail to address practical constraints and requirements of the electricity markets. As an example, most of the attack detection approaches have been established based on the DC economic dispatch, while the modern electricity markets may use AC models. This paper proposes a new anomaly detection tool based on graph theory using so-called egonets, specifically designed to detect false data injection attacks against electricity markets. In contrast with the conventional detection methods, the malicious intention of the adversaries is exploited in the design procedure. Therefore, the proposed approach can detect false data injection attacks, regardless of the attack type and power flow model.

Price-region bids in electricity markets

Current bid formats in pool-based electricity markets are ill-equipped to accommodate the broad range of non-conventional sources of flexibility, such as demand response and interconnected heating, natural gas and water infrastructure networks. To address this issue, this paper introduces the novel price-region bid format to be used in both forward electricity markets and financial right auctions. We show that price-region bids are able to accommodate a broad range of techno-economic characteristics, including complex spatial and temporal couplings, and facilitate market access to non-conventional flexibility providers. We then show that this new bid format is compatible with existing market structures, and satisfies desirable market properties under common assumptions. Three numerical studies are provided: two motivating examples based on a district heating utility and a cascaded hydro power plant, and a case study based on an integrated power and heat system. These studies illustrate the inability of existing bid formats to accommodate flexible resources, and show how price-region bids overcome this shortcoming.

Supervision mechanism of virtual bidding in electricity market: a review

In the reform of China‘s electricity market, most provinces adopt the centralized mode based on nodal pricing, thus the price convergence problem between day-ahead market and real-time market needs to be promoted. As a common practice in America, virtual bidding mechanism can promote price convergence, control market power and improve market efficiency. However, due to the financial nature of virtual bidding, virtual bidders may disrupt the market and obtain improper profits, thus damaging market efficiency and the interests of other participants. This paper summarizes the key characteristics of virtual bidding mechanisms in America, and analyzes the potential problems that by introducing virtual bidding into China. Then this paper summarizes the pre and post regulatory measures as well as credit mechanisms of virtual bidding in different electricity markets. In view of the cross-product manipulation through virtual bidding, this paper analyzes its principle and summarizes the regulation methods. Finally, considering the specific situation of China’s electricity market reform, this paper puts forward regulatory suggestions on implementing the virtual bidding mechanism in China.

Multi-agent simulation for strategic bidding in electricity markets using reinforcement learning

In this paper, Multi-agent Simulation (MAS) and Reinforcement Learning (RL) are studied, a theoretical framework of MAS is proposed for strategic bidding in electricity markets using reinforcement learning, which consists of two parts: one is a MAS system used to simulate the competitive bidding of the actual electricity market; the other is an adaptive learning strategy bidding system used to provide agents with more intelligent bidding strategies. An Experience-Weighted Attraction (EWA) reinforcement learning algorithm (RLA) is applied to the MAS model and a new MAS method is presented for strategic bidding in electricity markets using a new Improved EWA (IEWA). From both qualitative and quantitative perspectives it is compared with other three MAS methods using the Roth-Erev (RE), Q-learning and EWA. The result shows that the performance of the MAS method using IEWA is proved to be better than the others. The four MAS models using four RLAs are built for strategic bidding in electricity markets. Through running the four MAS models, the rationality and correctness of the four MAS methods are verified for strategic bidding in electricity markets using reinforcement learning.

Simplified model of integrated paper mill for optimal bidding in energy and reserve markets

Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill.

Strategic Convergence Bidding in Nodal Electricity Markets: Optimal Bid Selection and Market Implications

Convergence bidding (CB), a.k.a., virtual bidding (VB), is a market mechanism facilitated by Independent System Operators (ISOs) in wholesale electricity markets to help lower the gap between the prices in the day-ahead market (DAM) and prices in the real-time market (RTM). In this paper, we seek to answer two questions: 1) how can a strategic market participant maximize its profit when submitting CBs? 2) how can such strategically placed CBs affect the price gaps? Answering these questions is not straightforward but the results are insightful. The bidding problem in this context is a bi-level optimization, where the upper-level is about maximizing the profit for the convergence bidder and the lower-level is the economic dispatch problem. By solving the formulated bidding problem, we investigate the impact of strategic CBs on the DAM and RTM locational marginal prices (LMPs) under various practical scenarios. We demonstrate the scenarios under which a strategic CB, whether on its own, or when it is submitted jointly with a physical demand or physical supply bid, can or cannot work as intended, and result in decreasing or increasing the price gap in nodal electricity markets. We also examine how the performance of strategic CBs can be affected by uncertainty in demand or generation bids as well as physical contingencies in the power system, such as transmission line tripping. Special cases, such as net-zero convergence bidding are studied. The long-term performance of strategic convergence bidding is investigated. The above and other market implications are discussed in multiple case studies.

Constrained Generation Bids in Local Electricity Markets: A Semantic Approach

The worldwide investment in renewable energy sources is leading to the formation of local energy communities in which users can trade electric energy locally. Regulations and the required enablers for effective transactions in this new context are currently being designed. Hence, the development of software tools to support local transactions is still at an early stage and faces the challenge of constant updates to the data models and business rules. The present paper proposes a novel approach for the development of software tools to solve auction-based local electricity markets, considering the special needs of local energy communities. The proposed approach considers constrained bids that can increase the effectiveness of distributed generation use. The proposed method takes advantage of semantic web technologies, in order to provide models with the required dynamism to overcome the issues related to the constant changes in data and business models. Using such techniques allows the system to be agnostic to the data model and business rules. The proposed solution includes the proposed constraints, application ontology, and semantic rule templates. The paper includes a case study based on real data that illustrates the advantages of using the proposed solution in a community with 27 consumers.

A rolling horizon scheduling of aggregated electric vehicles charging under the electricity exchange market

The uncertainty of plug-in electric vehicle (EV) charging behaviour is a crucial factor that not only influences the peak power demand in distribution networks, but also the tariff plans of EV charging service. The uncertain upstream electricity price considerably complicates the issue regarding how to achieve specific economic goals for distribution network operators (DNOs) while guaranteeing EV users’ interest. A rolling horizon scheduling approach based on Genetic Algorithm (GA) is proposed in this paper to provide a win-win strategy for both DNOs and EV users. It deals with the online optimal scheduling problem of aggregated EVs in the energy exchange market. The objective of the scheduling strategy is to maximise DNOs’ profit margin by charging EVs in the low price time intervals as well as shifting peak charging loads. The operational constraints of EVs’ availability and electricity bidding are all considered in the time rolling horizon framework, meaning all this information will be updated, calculated and partially forecasted at each time interval until the end of the day. A case study is carried out with a 33-node distribution network to verify the effectiveness of the proposed scheduling strategy. In detail, specific tariff plans can be determined toward possible values of uncertain market price to satisfy utilities’ economic targets. In this way, both individuals and energy providers that participate in the energy market can benefit from the proposed rolling horizon strategy and keep the uncertainty under control.