Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

Modeling Energy Consumption in the Production Processes of Industrial Units Based on Load Response Programs in the Energy Market

The optimal operation of microgrids is of great significance for the sake of efficient and economical management of its energy resources.The microgrid energy management system should plan to operate the microgrid while simultaneously considering the electric and thermal load.The present study proposes energy management to minimize the costs of operating an industrial microgrid.In fact,planning for energy supply is among the critical issues that distribution companies deal with daily in the competitive environment.A distribution company usually meets customer(end customer)demands by purchasing energy from a wholesale market.Given the load curtailment,distribution companies have more choices and interactions in the market.Distribution companies face the two uncertainties of load changes and price fluctuations in their daily energy supply planning which could lead to the risk of loss resulting from the distribution company’s decision-making for daily energy supply planning.Thus,these companies face the challenge of maximizing profit in a risk-based environment.Therefore,the present study presents an optimal model of energy consumption in the production processes of aluminum and cement industrial units.The presented model was then used in planning the day-ahead energy of a microgrid containing these industrial units.Since the studied subject has many limitations,it would be difficult to solve it using mathematical methods.To resolve this issue then,the present study introduces a newly developed algorithm inspired by bee colonies.The proposed method seeks to significantly improve in the local and global search capabilities.In addition to confirming the validity of the proposed model,results indicate that the implementation of load-response programs and the cooperation of industrial units in the ancillary services and energy market can increase the profits of units and microgrids as well as correct the demand curve.According to the obtained results from the first and second test cases,the total profit of the aluminum unit was$188,103 and$237,805,respectively.Similarly,this profit for the cement industrial unit is$104,350 and$233,195.3,respectively.From the results,it can be observed that the final profit of the second unit has increased by 61%.

Modeling the Proposal of the Simultaneous Purchases and Sales of Electricity and Gas for the Energy Market in a Microgrid Using the Harmony Search Algorithm

The use of different energy carriers together,known as an energy hub,has been a hot topic of research in recent years amongst scientists and researchers.The term energy hub refers to the simultaneous operation of various infrastructures for energy generation and transfer,which has gained momentum in the form of microgrids(MGs).This paper introduces a new strategy for the optimal performance of an MG consisting of different energy carriers for each day.In a smart distribution network(DN),MGs can reduce their own costs in the previous-day market by bidding on sales and purchases.The sales and purchases bidding problem is challenging due to different uncertainties,however.This paper proposes a two-stage strategy for making an optimal bid on electricity sales and purchases with electricity and gas price dependency in the previous-day and real-time markets for an energy hub.In this model,the MG behavior regarding the electricity and gas energy sales/purchase,the simultaneous effects of electricity and gas prices,as well as the energy carriers’dependence on one another are all examined.Due to the inherent uncertainty in the sources of clean energy production,the probabilistic model and the production and reduction scenario have been used in the paper to cover this issue.In the proposed grid,energy sales/purchases are presented in a multi-carrier MG in a two-stage model.This model is solved by using the harmony search algorithm in MATLAB.Numeric results demonstrate the benefits of this model in reducing energy hub costs of operation.

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Sharing Economy in Local Energy Markets

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost recovery.With the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent years.Thus,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential practices.This paper can provide a useful reference and insights for the activation of demand-side flexibility potential.Hopefully,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

COVID‑19 pandemic and the crude oil market risk:hedging options with non‑energy financial innovations

This study examines the hedging effectiveness of financial innovations against crude oil investment risks,both before and during the COVID-19 pandemic.We focus on the non-energy exchange traded funds(ETFs)as proxies for financial innovations given the potential positive correlation between energy variants and crude oil proxies.We employ a multivariate volatility modeling framework that accounts for important statistical features of the non-energy ETFs and oil price series in the computation of optimal weights and optimal hedging ratios.Results show evidence of hedging effectiveness for the financial innovations against oil market risks,with higher hedging performance observed during the pandemic.Overall,we show that sectoral financial innovations provide resilient investment options.Therefore,we propose that including the ETFs in an investment portfolio containing oil could improve risk-adjusted returns,especially in similar financial crisis as witnessed during the pandemic.In essence,our results are useful for investors in the global oil market seeking to maximize risk-adjusted returns when making investment decisions.Moreover,by exploring the role of structural breaks in the multivariate volatility framework,our attempts at establishing robustness for the results reveal that ignoring the same may lead to wrong conclusions about the hedging effectiveness.

Modelling and Numerical Valuation of Power Derivatives in Energy Markets

In this work we investigate the pricing of swing options in a model where the underlying asset follows a jump diffusion process.We focus on the derivation of the partial integro-differential equation(PIDE)which will be applied to swing contracts and construct a novel pay-off function from a tree-based pay-off matrix that can be used as initial condition in the PIDE formulation.For valuing swing type derivatives we develop a theta implicit-explicit finite difference scheme to discretize the PIDE using a Gaussian quadrature method for the integral part.Based on known results for the classical theta-method the existence and uniqueness of solution to the new implicit-explicit finite difference method is proven.Various numerical examples illustrate the usability of the proposed method and allow us to analyse the sensitivity of swing options with respect to model parameters.In particular the effects of number of exercise rights,jump intensities and dividend yields will be investigated in depth.

Reinforcement learning-driven local transactive energy market fordistributed energy resources

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.

Optimal Offering of Energy Storage in UK Day-Ahead Energy and Frequency Response Markets

The offering strategy of energy storage in energy and frequency response(FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors(deterministic one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

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.

Electricity-Heat-Based Integrated Demand Response Considering Double Auction Energy Market with Multi-Energy Storage for Interconnected Areas

With development of integrated energy systems and energy markets,transactive energy has received increasing attention from society and academia,and realization of energy distribution and integrated demand response through market transactions has become a current research hotspot.Research on optimized operation of a distributed energy station as a regional energy supply center is of great significance for improving flexibility and reliability of the system.Based on retail-side energy trading market,this study first establishes a framework of combined electric and heating energy markets and analyses a double auction market mechanism model of interconnected distributed energy stations.This study establishes a mechanism model of energy market participants,and establishes the electric heating combined market-clearing model to maximize global surplus considering multi-energy storage.Finally,in the case study,a typical user energy consumption scenario in winter is selected,showing market-clearing results and demand response effects on a typical day.Impact of transmission line constraints,energy supply equipment capacity,and other factors on clearing results and global surplus are compared and analyzed,verifying the effects of the proposed method on improving global surplus,enhancing interests of market participants and realizing coordination and optimal allocation of both supply and demand resources through energy complementarity between regions.

Green Energy Development System under the Background of Environmental Sustainability

With the continuous advancement of economic globalization,energy demand is expanding and energy consumption is excessive,which leads to energy shortage.Unreasonable energy use also brings great challenges to the environment and affects the balance of the ecosystem seriously.The rise of the third industrial revolution has injected new vitality into energy system.The construction of energy Internet system,which integrates Internet technology and energy technology,has become a new energy system of sustainable development.It has put forward the reform scheme for the mismatch of energy demand points and environmental pollution.The deepening of sustainable development strategy accelerates the promotion of energy Internet system,promotes the large-scale utilization and sharing of renewable energy.It also provides a new idea for realizing green energy structure and efficient energy utilization.Firstly,based on the core concept of energy Internet system,this paper reviews the relevant research on energy Internet system in academic and industrial circles,and gives the preliminary definition of energy Internet system market.On this basis,it puts forward the development goal of energy Internet system market.It compares the new energy Internet system market with the traditional single energy market,highlighting the characteristics of energy Internet system market,it is reflected in the diversified transformation of market trading subjects,the diversification of market trading objects,the increasing dependence of information technology and the improvement of the matching degree of supply and demand.Then,the integrated architecture of of the energy Internet system market is carried out,and the issues related to the transaction mode and operation mechanism of the energy Internet system market are discussed.Finally,the frontier problems of energy Internet system market are summarized,and the research prospect of energy Internet system market is put forward.

Energy-Related Services as a Business:Eco-Transformation Logic to Support the Low-Carbon Transition

The transition to low-carbon development has been recognized as one of the most important directions for the transformation of national economies in most countries.Today we can identify two of the most common service-oriented logical models of this transition.These are models of Service-Dominant Logic and Product-Service Systems that are used in this study to create a service-oriented platformfor stimulating business development in the field of energy-related(ER)services.The establishment of such a platform is being considered based on the conceptual provisions of the global business ecosystem.Such a platform has been found to be a powerful tool for systemic coordinated cooperation between actors in the ER-services market and the relevant actors in the energy sector as well as in other industrial markets,unleashing the benefits of synergies,resulting from such cooperation.The logical models of attracting energy services companies to the creation of virtual communities(centers)for large-scale implementation of measures in the field of energy efficiency and renewable energy sources are presented in detail.The advantages of the practical implementation of logicalmodels are confirmed by calculations performed onmathematicalmodel adapted for engineering use in the field of ER-services.

Solution to Coordination of Transmission and Distribution for Renewable Energy Integration into Power Grids:An Integrated Flexibility Market

Nowadays,transmission system operators(TSOs)encounter the challenges of securely operating power grids with high penetration of renewables.In this context,more flexibility is needed than ever to maintain system reliability.With rapid development of coordinated transmission and distribution technology recently,active distribution networks(ADNs)which have abundant flexible resources,have the potential to provide flexibility to the TSO.Hence,an integrated transmission-distribution flexibility market is proposed in this paper.In the proposed framework,the energy market is first cleared to obtain baseline generation schedules.Then the baseline generation schedules are treated as inputs to the flexibility market.Furthermore,the integrated flexibility market is cleared to facilitate flexibility trading between transmission and distribution networks.To preserve the data privacy of different market participants,an alternating direction method of multipliers(ADMM)based method is utilized to clear the markets in a distributed manner.In the energy market and flexibility market-clearing model,transmission network market clearing models are linear programming(LP)models,and distribution network market clearing models are second order cone programming(SOCP)models.Through the proposed method,only limited information is exchanged between TSO and DSOs.Case studies are conducted on a revised IEEE 30-bus transmission network with two 33-node ADNs.Numerical results demonstrate the proposed flexibility market framework can enhance competitiveness of generation companies in the transmission network and distributed generators(DGs)in the ADNs.Moreover,flexibility purchasing cost is reduced by 17.7%compared to traditional flexibility supplied by generators according to the case study results,and ADNs can gain additional flexibility profit by providing flexibility.

Two-stage stochastic-robust model for the self-scheduling problem of an aggregator participating in energy and reserve markets

This paper addresses a two-stage stochastic-robust model for the day-ahead self-scheduling problem of an aggrega-tor considering uncertainties.The aggregator,which integrates power and capacity of small-scale prosumers and flex-ible community-owned devices,trades electric energy in the day-ahead(DAM)and real-time energy markets(RTM),and trades reserve capacity and deployment in the reserve capacity(RCM)and reserve deployment markets(RDM).The ability of the aggregator providing reserve service is constrained by the regulations of reserve market rules,including minimum offer/bid size and minimum delivery duration.A combination approach of stochastic program-ming(SP)and robust optimization(RO)is used to model different kinds of uncertainties,including those of market price,power/demand and reserve deployment.The risk management of the aggregator is considered through con-ditional value at risk(CVaR)and fluctuation intervals of the uncertain parameters.Case studies numerically show the economic revenue and the energy-reserve schedule of the aggregator with participation in different markets,reserve regulations,and risk preferences.

Model Predictive Control Strategy for Residential Battery Energy Storage System in Volatile Electricity Market with Uncertain Daily Cycling Load

This paper presents a control strategy for residential battery energy storage systems,which is aware of volatile electricity markets and uncertain daily cycling loads.The economic benefits of energy trading for prosumers are achieved through a novel modification of a conventional model predictive control(MPC).The proposed control strategy guarantees an optimal global solution for the applied control action.A new cost function is introduced to model the effects of volatility on customer benefits more effectively.Specifically,the newly presented cost function models a probabilistic relation between the power exchanged with the grid,the net load,and the electricity market.The probabilistic calculation of the cost function shows the dependence on the mathematical expectation of market price and net load.Computational techniques for calculating this value are presented.The proposed strategy differs from the stochastic and robust MPC in that the cost is calculated across the market price and net load variations rather than across model constraints and parameter variations.

Optimal SOC Headroom of Pump Storage Hydropower for Maximizing Joint Revenue from Day-ahead and Real-time Markets Under Regional Transmission Organization Dispatch

In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision potentials.Inspired by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA market.Then,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom model.Specifically,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two markets.Numerical case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

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.

The operating schedule for battery energy storage companies in electricity market

This paper presents a series of operating schedules for Battery Energy Storage Companies(BESC)to provide peak shaving and spinning reserve services in the electricity markets under increasing wind penetration.As individual market participants,BESC can bid in ancillary services markets in an Independent System Operator(ISO)and contribute towards frequency and voltage support in the grid.Recent development in batteries technologies and availability of the day-ahead spot market prices would make BESC economically feasible.Profit maximization of BESC is achieved by determining the optimum capacity of Energy Storage Systems(ESS)required for meeting spinning reserve requirements as well as peak shaving.Historic spot market prices and frequency deviations from Australia Energy Market Operator(AEMO)are used for numerical simulations and the economic benefits of BESC is considered reflecting various aspects in Australia’s National Electricity Markets(NEM).

A Markov-switching dynamic regression analysis of the asymmetries related to the determinants of US crude oil production between 1982 and 2019

The structural changes brought about by shale oil revolution have inspired this paper of which the aim is to analyze the potential asymmetries related to the determinants of crude oil production in the USA.Thus,using a Markov-switching dynamic regression model in which parameters change when oil production moves from one regime to the other,it is found that for both oil production and oil relative importance,the regime that was dominant during the 1980s and the early 1990s when oil production in the USA was substantially high is the same regime that has once again become dominant in the decade corresponding to the shale oil revolution.Furthermore,the study reveals the existence of asymmetries in the relationship between US crude oil production and both manufacturing production and the consumer price index.Asymmetries are also found in the relationship between the relative importance US crude oil and manufacturing production.Finally,it is found that the intercept and the variance parameter also vary from one regime to the other,thus justifying the use of regime-dependent models.