Optimal Bidding Strategy for PV and BESSs in Joint Energy and Frequency Regulation Markets Considering Carbon Reduction Benefits

Photovoltaic(PV)and battery energy storage systems(BESSs)are key components in the energy market and crucial contributors to carbon emission reduction targets.These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading.This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets,with a specific focus on carbon reduction benefits.A two-stage bidding framework that optimizes the profit of PV and BESSs is presented.In the first stage,the day-ahead energy market takes into account potential real-time forecast deviations.In the second stage,the real-time balancing market uses a rolling optimization method to account for multiple uncertainties.Notably,a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control(AGC).This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market.This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error(ACE)occurs.The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.

Bidding Strategy of a Virtual Power Plant Considering Carbon-electricity Trading

Virtual power plant(VPP)aggregates large amounts of distributed energy and controllable loads.The comprehensive consideration of carbon emissions and electricity transactions has great significance in improving the VPP operation’s economic efficiency.In this paper,the bidding strategy of the VPP by considering the carbon-electricity integration trading in an auxiliary service(AS)market is studied.First of all,the basic structure and operating features of the VPP are briefly introduced.Then,the bidding strategy model of carbon-electricity integration trading in an auxiliary service market is proposed and the corresponding objective function and the constraint conditions are also analyzed.Furthermore,the GAMS solver is utilized to give the optimal solution of the bidding strategy model.Finally,the effectiveness of the bidding strategy of a VPP based on the consideration of carbon-electricity integration trading is verified through simulation cases.

Dispatch and bidding strategy of active distribution network in energy and ancillary services market

The active distribution network(ADN)is able to manage distributed generators(DGs),active loads and storage facilities actively.It is also capable of purchasing electricity from main grid and providing ancillary services through a flexible dispatching mode.A competitive market environment is beneficial for the exploration of ADN’s activeness in optimizing dispatch and bidding strategy.In a bilateral electricity market,the decision variables such as bid volume and price can influence the market clearing price(MCP).The MCP can also have impacts on the dispatch strategy of ADN at the same time.This paper proposes a bilevel coordinate dispatch model with fully consideration of the information interaction between main grid and ADN.Simulation results on a typical ADN validate the feasibility of the proposed method.A balanced proportion between energy market and ancillary services market can be achieved.

A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information

The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.

Bidding Strategy for Coordinated Operation of Wind Power Plants and NGG-P2G Units in Electricity Market

The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity market.Within this context,this paper proposes a bi-level stochastic optimization model for the joint operation of a coordinated wind power plant(WPP)and natural gas generating(NGG)-power to gas(P2G)suppliers participating in the day-ahead(DA)market and real-time(RT)market as well as providing real-time auxiliary services.The coordinated supplier’s payoff is maximized in the upper level with consideration of the uncertainties of WPP output capacity and RT electricity price,while the social welfare of the grid is maximized in the lower level.Simulation results demonstrate the effectiveness of the proposed bidding model of the coordinated WPPs and NGG-P2G suppliers by examining its bidding behaviors and benefits with comparisons of four other bidding models.

Commission Strategy of the Auction House

In a SIPV model, when the commission proportion is not certain, but related with bargain price, generally, it is a linear function of the bargain price, this paper gives bidders＇ equilibrium bidding strategies in the first-and secondprice auctions. We find that the equilibrium strategies in second-price auction are dominant strategies. For seller or auction house, whether the fixed proportion or the unfixed proportion is good is not only related with constant item and the linear coefficient of the linear function, the size of the fixed commission proportion, but also related with the value of the item auctioned. So, in the practical auctions, the seller and the auction house negotiated with each other to decide the commission rules for their own advantage.

A method for power suppliers’optimal cooperative bidding strategies considering network losses

The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.

Market Power of Coordinated Hydro-wind Joint Bidding:Croatian Power System Case Study

The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.

Distributed Photovoltaic Generation in the Electricity Market:Status,Mode and Strategy

With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.

Strategic Bidding in Distribution Network Electricity Market Focusing on Competition Modeling and Uncertainties

Developing the electricity market at the distribution level can facilitate the energy transactions in distribution networks with a high penetration level of distributed energy resources(DERs)and microgrids(MGs).However,the lack of comprehensive information about the marginal production cost of competitors leads to uncertainties in the optimal bidding strategy of participants.The electricity demand within the network and the price in the wholesale electricity market are two other sources of the uncertainties.In this paper,a day-ahead-market-based framework for managing the energy transactions among MGs and other participants in distribution networks is introduced.A game-theory-based method is presented to model the competition and determine the optimal bidding strategy of participants in the market.Robust optimization technique is employed to capture the uncertainties in the marginal cost of competitors.Additionally,the uncertainties in demand are modeled using a scenario-based stochastic approach.The results ob-tained from case studies reveal the merit of considering competition modeling and uncertainties.

A Deep Reinforcement Learning Bidding Algorithm on Electricity Market

In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.

Strategic Peer-to-peer Energy Trading Framework Considering Distribution Network Constraints

With the development of smart home energy management technology,prosumers are endowed with increased initiative in peer-to-peer(P2P)transactions,bringing new potential for cost savings.In this study,a novel strategic P2P energy trading framework is proposed considering the impact of network constraints on personal transaction strategies.Prosumers can estimate the allowed power injection before engaging in the P2P energy trading,which is solved in a distributed manner based on the sharing form alternating direction method of multipliers(ADMM)algorithm.To quantify the network usage cost for each prosumer and promote local transactions among prosumers at the same bus,a modified continuous double auction(CDA)matching algorithm is proposed including a transaction fee.An adaptive aggressiveness-based bidding strategy is generated considering the risk of uncertainty in real-time energy delivery amount under the limitations of the distribution network.The proposed strategic P2P energy trading framework is tested with the IEEE 37-bus distribution network and it is effective in creating profits for prosumers and supporting distribution network operations.

Coordinated Operation of Concentrating Solar Power Plant and Wind Farm for Frequency Regulation

As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.

Bi-level optimization based two-stage market clearing model considering guaranteed accommodation of renewable energy generation

The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment,where the low cost renewable energy generation is not reflected in the high bidding price of high cost conventional energy generation.This study addresses this issue by proposing a bi-level optimization based two-stage market clearing model that considers the bidding strategies of market players,and guarantees the accommodation of renewable energy generation.The first stage implements a dual-market clearing mechanism that includes a unified market for trading the power generations of both renewable energy and conventional energy units,and a subsidy market reserved exclusively for conventional generation units.A re-adjustment clearing mechanism is then proposed in the second stage to accommodate the power generation of remaining renewable energy units after first stage energy allocations.Each stage of the proposed model is further described as a bi-level market equilibrium problem and is solved using a co-evolutionary algorithm.Finally,numerical results involving an improved IEEE 39-bus system dem-onstrate that the proposed two-stage model meets the basic requirements of incentive compatibility and individual rationality.It can facilitate the rational allocation of resources,promote the economical operation of electric power grids,and enhance social welfare.