Evolutionary game-based optimization of green certificate-carbon emission right-electricity joint market for thermal-wind-photovoltaic power system

With the increasing proportion of renewable energy in the power market,the demands on government financial subsidies are gradually increasing.Thus,a joint green certificate-carbon emission right-electricity multi-market trading process is proposed to study the market-based strategy for renewable energy.Considering the commodity characteristics of green certificates and carbon emission rights,the dynamic cost models of green certificates and carbon rights are constructed based on the Rubinstein game and ladder pricing models.Furthermore,considering the irrational bidding behavior of energy suppliers in the actual electricity market,an evolutionary game based multi-market bidding optimization model is presented.Subsequently,it is solved using a composite differential evolutionary algorithm.Finally,the case study results reveal that the proposed model can increase profits and the consumption rate of renewable energy and reduce carbon emission.

Daily rolling estimation of carbon emission cost of coal-fired units considering long-cycle interactive operation simulation of carbon-electricity market

The high overlap of participants in the carbon emissions trading and electricity markets couples the operations of the two markets.The carbon emission cost(CEC)of coal-fired units becomes part of the power generation cost through market coupling.The accuracy of CEC calculation affects the clearing capacity of coal-fired units in the electric power market.Study of carbon–electricity market interaction and CEC calculations is still in its initial stages.This study analyzes the impact of carbon emissions trading and compliance on the operation of the electric power market and defines the cost transmission mode between the carbon emissions trading and electric power markets.A long-period interactive operation simulation mechanism for the carbon–electricity market is established,and operation and trading models of the carbon emissions trading market and electric power market are established.A daily rolling estimation method for the CEC of coal-fired units is proposed,along with the CEC per unit electric quantity of the coal-fired units.The feasibility and effectiveness of the proposed method are verified through an example simulation,and the factors influencing the CEC are analyzed.

Inter-Provincial Transaction Model in Two-Level ElectricityMarket Considering Carbon Emission and Consumption Responsibility Weights

In the context of the joint operation of China’s intra-provincial markets and inter-provincial trading,how to meet the load demand and energy consumption using inter-provincial renewable energy trading is a key problem.The combined operation of intra-provincial and inter-provincial markets provides a new way for provincial power companies to optimize and clear the intra-provincial power market,complete the intra-provincial consumption responsibility weight index,and consume renewable energy across provinces and regions.This paper combines power generation and consumption within the province,uses inter-provincial renewable energy trading tomeet the load demand within the province and completes the index of intra-provincial consumption responsibility weights.The intra-provincial market trading and inter-provincial market clearing are respectively taken as the upper and lower levels of the model.Under the two-level electricity market operation framework,the upper-level model aims to minimize the expected total operating cost within the province considering the carbon emission cost and the weight of the consumption responsibility,while the lower-level model aims to minimize the inter-provincial renewable energy purchasing cost.Finally,the influence of inter-provincial transaction mechanism,risk aversion coefficient,voucher price,and responsibility weight on operating cost is analyzed.Simulation is used to verify that the proposed model can meet the requirements of the provincial load power consumption and the consumption responsibility weight index,and promote the consumption of renewable energy.

Economy supervision mode of electricity market and its incentive mechanism

The analysis of a supervision environment is the first step for a company to enter the new electricity market. Transmission and distribution assets are the main investment targets of a company. The overseas power market belongs to the regulated industry;whether it is a stock M&A project or a green land bidding project, the regulatory environment determines the assets. The level of return and investment risk that guides the operation strategy of existing overseas assets, has a significant impact on the investment and operations of international companies. A comprehensive and rapid assessment of the regulatory environment can help the project teams of international companies understand the macroenvironment of the target electricity market within a short period, quickly identify investment risks, qualitatively analyze the return level of the underlying assets, shorten the decision time, capture investment opportunities, and enhance the team. Efficiency and quality of work are factors of great importance.

Joint electricity and carbon market for Northeast Asia energy interconnection

Decarbonization of the electricity sector is crucial to mitigate the impacts of climate change and global warming over the coming decades.The key challenges for achieving this goal are carbon emission trading and electricity sector regulation,which are also the major components of the carbon and electricity markets,respectively.In this paper,a joint electricity and carbon market model is proposed to investigate the relationships between electricity price,carbon price,and electricity generation capacity,thereby identifying pathways toward a renewable energy transition under the transactional energy interconnection framework.The proposed model is a dynamically iterative optimization model consisting of upper-level and lower-level models.The upper-level model optimizes power generation and obtains the electricity price,which drives the lower-level model to update the carbon price and electricity generation capacity.The proposed model is verified using the Northeast Asia power grid.The results show that increasing carbon price will result in increased electricity price,along with further increases in renewable energy generation capacity in the following period.This increase in renewable energy generation will reduce reliance on carbon-emitting energy sources,and hence the carbon price will decline.Moreover,the interconnection among zones in the Northeast Asia power grid will enable reasonable allocation of zonal power generation.Carbon capture and storage (CCS) will be an effective technology to reduce the carbon emissions and further realize the emission reduction targets in 2030-2050.It eases the stress of realizing the energy transition because of the less urgency to install additional renewable energy capacity.

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

Experiences of the Electricity System Operator Incentive Scheme in Great Britain

National Grid is the electricity system operator in Great Britain and has an unique feature in so far as it is one of the world’s few for-profit system operators. In addition, the commercially orientation of the British market rules means that nearly every action taken by National Grid to operate the system has a cost associated to it. Based on those factors and in order to encourage National Grid to seek continuous improvements and drive for efficient and economic system operation, the regulator (Ofgem) offers an incentive scheme, whereby a target is agreed annually and any savings in relation to this target are shared between consumers and National Grid in the form of a profit. It is in National Grid’s best interest to have mechanisms to mitigate the impacts of volatility in the costs it faces as system operator so that it can implement cost saving actions without the risk of windfall losses (or gains) arising from sudden changes in uncontrollable drivers. The purpose of this paper is to share the experiences of National Grid in the operation of Great Britain's electricity system, with a special interest on the mechanisms created to manage the associated costs in response to the incentive scheme. It does so by describing the market operation in Great Britain and the costs drivers impacting National Grid’s system operation and illustrating the steps recently taken by National Grid to propose volatility mitigation mechanisms. It concludes with the rationale and expected results from the latest proposals as consulted with the industry for introduction in the incentive scheme starting on 1st April 2011. It is worth noting that with this work, the authors wish to both share the experience with other system operators and regulators in the world, as well as give British market participants an insight on the inner workings of National Grid.

Multi scale risk measurement in electricity market:a wavelet based value at risk approach

Value at risk (VaR) is adopted to measure the risk level in the electricity market. To estimate VaR at higher accuracy and reliability, the wavelet variance decomposed approach for value at risk estimates (WVDVaR) is proposed. Empirical studies conduct in five Australian electricity markets, which evaluate the performances of both the proposed approach and the traditional ARMA-GARCH approach using the Kupiec backtesting procedure. Experimental results suggest that the proposed approach measures electricity market risks at higher accuracy and reliability than the bench mark ARMA-GARCH approach, as indicated by the higher p values during the Kupiec backtesting procedure. In addition, the new approach also provides more insight into the risk evolution process over time and helps in adjusting VaR estimates to the time horizons that best suit investor interests. The distribution of risk according to investor preferences is shown by decomposing VaR across different time horizons. This also provides important information for the appropriate aggregation of risk measures based on investor investment preferences.

Sentiment Analysis as a Source of Gaining Competitive Advantage on the Electricity Markets

The electricity retail markets are evolving toward more competitive and customer-oriented. The deployment of smart meters and a wealth of new technologies create customers＇ eagerness for taking control of their electricity consumption. By being better-informed about the energy usage, people are encouraged to switch deals among existing suppliers or move to a new energy provider. Moreover, as customers are more socially interconnected, the Internet portals and social media become a place for discussion, comparison, and evaluation of the available offers. Unfortunately, in case of the energy sector there is a lack of understanding that such information, when taken into account and properly analyzed, can be a completely new and a powerful source of competitive advantage. In the paper, we introduce a solution that the use of quasi real-time automated sentiment analysis on the energy suppliers and the relevant aspects of their offers may enable energy companies to adapt quickly to changing circumstances, prevent potential customer churn, and harness new business opportunities.

Management of Peak Loads in an Emerging Electricity Market

An electricity market is a trading platform provided by the actors in the electricity sector to sell and buy electricity while maintaining the stability of the transmission network and minimizing energy losses.The management of electrical energy for rational use consists of all the operations that the consumers can carry out in order to minimize their electricity bill,while the producers optimize their benefits and the transmission infrastructure.The reduction of active and reactive power consumption and the smoothing of daily and yearly load profiles are the main objectives in this work.Many developed countries already have properly functioning electricity markets,but developing countries are still in their infancy of deregulated electricity markets.The major tools used in smoothing the load profiles include decentralized generation,energy storage and demand response.A load power smoothing control strategy is proposed to smooth the load power fluctuations of the distribution network.The required power change is determined by evaluating the power fluctuation rate of the load,and then the required power change is allocated to some generators or to some stored reserves.Otherwise,the consumers are made to curtail their power consumption.The ideas proposed in this work provide important opportunities for energy policy makers and regulators.These ideas would only be feasible if there exists real-time communication among the actors in the electricity market.The results indicate that as much as 1100 Megawatt-hours of energy can be stored for smoothing the load profile,when applied to the Southern Interconnected Grid of the Cameroon power system;and that Time of Use(TOU)pricing could be used instead of rotating blackouts in case of energy shortage.

Optimal Decision-Making of Trans-Provincial Electricity Market Subjects with Risks under Renewable Portfolio Standards

The randomness and uncertainty of renewable energy generation are expected to significantly change the optimal decision-making of trans-provincial electricity market subjects.Therefore,it is beneficial to optimize the interests of each of these subjects,considering the unpredictable risks of renewable energy under the renewable portfolio standards(RPS)and researching their effects on the optimal decision-making of transprovincial electricity market multi-subjects.First,we develop a trans-provincial trading market mechanism for renewable energy and clarify the electricity supply and demand relation and the green certificates supply and demand relation of trans-provincial electricitymarketmulti-subjects.Then,under the RPS,we construct a multi-subject game model of the power supply chain that recognizes the risks,and adopt the reverse induction method to discuss the optimum risk-taking judgment of each subject in the trans-provincial electricity market.Finally,we useMATLAB to verify the viability and efficacy of the proposed gamemodel,and obtain a certain reference value for the optimal decision-making of trans-provincial electricity market subjects.In summary,we consider the uncertainty risks of renewable energy under RPS,study the effects of the green certificate price and risk aversion coefficient in the RPS mechanism on the optimal decisionmaking of trans-provincial electricity market subjects,and obtain the changing trends of two different power products and those of different electricity market subjects under the influence of the green certificate price and risk aversion coefficient,which have a certain reference value for studying the factors affecting the optimal decision-making of trans-provincial electricity market subjects.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

Daily Generation Strategies with Consideration of Electricity Contract and Spinning Reserve

When programming the daily generation strategies, power plants consider not only the competitive bids in spot market and the original purchase contracts with distribution companies or users, but also the spinning reserve. Aiming at this goal, the paper puts forward a daily generating model, which is a nonlinear programming model containing integral and continous variables, to coordinate the power volumes of competitive bids, contract and the spinning reserve of power plants and thus to maximize their profits. Then, the model is simplified to ease its solving process, and solved by priority ranking method based on efficiency and quadratic programming. The simulation result of a case study shows that the proposed scheme is reasonable and practicable.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

A Novel Approach for a Two Area Load Frequency Control in a Competitive Electricity Market Adopting Unscheduled Interchange Price

This paper proposes a novel price based load frequency control scheme for a two area system, using an Unscheduled Interchange (UI) price signal, which is ideal and suitable for electricity market. The Government of India has introduced Availability Based Tariff (ABT) structure with an intention of ensuring grid security and to regulate grid indiscipline through Central Electricity Regulatory Commission Regulation. After the introduction of this regulation, the over or under injection by the generating companies (GENCOs) during off peak or peak hours are demoralized and the frequency is maintained at the nominal value. In this paper, the GENCOs instead of reacting to this price signal manually, an automated mode of frequency control is deployed in each area using UI price signal to achieve fast response to load change. The Distribution Company (DISCO) Participation Matrix (DPM) has also been employed in this work to relate the scenario under deregulation of electricity market. The proposed scheme has been verified for different cases by simulating it on a two area system, each having four GENCOs and one DISCO in MATLAB/SIMULINK environment. From the results, it is clearly observed that if the proposed method is employed by all GENCOs, it will certainly enhance the control of system frequency and at the same time throw down the UI liability of market participants. It also ensures that the GENCOs and DISCOs strictly adhere to the bilateral contract following the DISCO participation matrix.

2008 Annual Report of SGCC on Electricity Market Transactions

On April 14,2009,the State Grid Corporation of China (SGCC) held the press conference in Beijing on electricity market transactions and then released the "2008 Annual Report of SGCC on Electricity Market Transactions".

A Joint Optimization Algorithm for Renewable Energy System

Energy sustainability is a hot topic in both scientific and political cir-cles.To date,two alternative approaches to this issue are being taken.Some peo-ple believe that increasing power consumption is necessary for countries’economic and social progress,while others are more concerned with maintaining carbon consumption under set limitations.To establish a secure,sustainable,and economical energy system while mitigating the consequences of climate change,most governments are currently pushing renewable growth policies.Energy mar-kets are meant to provide consumers with dependable electricity at the lowest pos-sible cost.A profit-maximization optimal decision model is created in the electric power market with the combined wind,solar units,loads,and energy storage sys-tems,based on the bidding mechanism in the electricity market and operational principles.This model utterly considers the technological limits of new energy units and storages,as well as the involvement of new energy and electric vehicles in market bidding through power generation strategy and the output arrangement of the virtual power plant’s coordinated operation.The accuracy and validity of the optimal decision-making model of combined wind,solar units,loads,and energy storage systems are validated using numerical examples.Under multi-operating scenarios,the effects of renewable energy output changes on joint sys-tem bidding techniques are compared.

Transitional Revenue Scheme for High-cost Generator Unit in Guangdong Electricity Market Based on Government Authorized Contracts

High-cost generator units are at a cost disadvantage in electricity spot markets.This study focuses on revenue mechanisms of gas-fired units affected by power market reform in Guangdong province,China.For the first time,we compare impacts on market indicators of five settlement mechanisms:feed-in tariff(FiT),location marginal price(LMP),contract for difference(CFD),direct subsidy(DS),and estimated revenue method(ERM).In particular,we design key parameters,including authorized CFD and ERM of two kinds of government authorized contracts based on traditional dispatching patterns to avoid significant profit fluctuations brought by market reforms.We also analyze impact of factors such as climbing performance,seasonal load,and subsidy amount on the overall market and its players.Results of a case study show to directly subsidize gasfired units will lead higher-cost units to generate more electricity,with a resulting loss of social welfare.Disruption of market prices and provision of unreasonable incentives are fatal disadvantages of this subsidy method.The government and policymakers should consider financial means to adjust benefits to reduce production costs and increase social welfare.Also,by case analysis,the ERM shows its stable performance in revenue of high-cost units,while we find that authorized CFD is not applicable for gas-fired units whose output is unstable as a marginal unit frequently.Therefore,we suggest government agencies adopt ERM to sign contracts with gas-fired units,to attain a balance between fairness and efficiency.

State-of-the-Art Analysis and Perspectives for Peer-to-Peer Energy Trading

As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with distributed energy resources(DERs)are able to directly trade and share energy with each other.This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers,research projects,and industrial practice.Key aspects in P2P energy trading are identified and discussed,including market design,trading platforms,physical infrastructure and information and communication technology(ICT)infrastructure,social science perspectives,and policy.For each key aspect,existing research and practice are critically reviewed and insights for future development are presented.Comprehensive concluding remarks are provided at the end,summarizing the major findings and perspectives of this paper.P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.