Chain Rule for Extracting Resilience Procurement Costs from Locational Marginal Prices

Power system resilience procurement costs in N-k contingencies have gained more prominence as number of extreme events continues to increase.A chain rule is presented in this paper for extracting resilience procurement costs from a fully decomposed locational marginal price(LMP)model.First,power transfer distribution factor(PTDF)matrices with AC power flow(i.e.,AC-PTDF)are determined.AC-PTDF and AC-LODF(line outage distribution factor)equations are derived for N-k contingencies and a fully decomposed LMP model is developed where generation and transmission security components are established for specific contingencies.Furthermore,resilience procurement costs can be measured at different buses for the proposed security components.Impact of N-k contingencies on resilience procurement costs at specific buses can be determined as proposed security components will gain more insight for resilience procurement in power systems.The modified IEEE 6-bus and 118-bus systems are adopted to verify effectiveness of the proposed resilience procurement method.

Impact of power system network topology errors on real-time locational marginal price

This paper examines the impact of power transmission network topology change on locational marginal price(LMP) in real-time power markets. We consider the case where the false status of circuit breakers(CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch.The main goal of this paper is to assess the economic impact of this misconfigured network topology on realtime LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding prices for marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.

Shapley value cooperative game theory-based locational marginal price computation for loss and emission reduction

An iterative method based on Shapley Value Cooperative Game Theory is proposed for the calculation of local marginal price (LMP) for each Distributed Generator (DG) bus on a network. The LMP value is determined for each DG on the basis of its contribution to reduce loss and emission reduction, which is assessed using the Shapley Value approach. The proposed approach enables the Distribution Company (DISCO) decision-maker to operate the network optimally in terms of loss and emission. The proposed method is implemented in the Taiwan Power Company distribution network 7 warnings consisting of 84 buses and 11 feeders in the MATLAB environment. The results show that the proposed approach allows DISCO to operate the network on the basis of its priority between the reduction of active power loss and emission in the network.

Formulation of Locational Marginal Electricity-carbon Price in Power Systems

Decarbonisation of power systems is essential for realising carbon neutrality,in which the economic cost caused by carbon needs to be qualified.Based on the formulation of locational marginal price(LMP),this paper proposes a locational marginal electricity-carbon price(EC-LMP)model to reveal carbon-related costs caused by power consumers.A carbon-priceintegrated optimal power flow(C-OPF)is then developed to maximise economic efficiency of the power system considering the costs of electricity and carbon.Case studies are presented to demonstrate the new formulation and results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy.

An approach to Iocational marginal price based zonal congestion management in deregulated electricity market

Congestion of transmission line is a vital issue and its management pose a technical challenge in power system deregulation. Congestion occurs in deregulated electricity market when transmission capacity is not sufficient to simultaneously accommodate all constraints of power transmission through a line. Therefore, to manage congestion, a locational marginal price （LMP） based zonal congestion management approach in a deregulated elec- tricity market has been proposed in this paper. As LMP is an economic indicator and its difference between two buses across a transmission line provides the measure of the degree of congestion, therefore, it is efficiently and reliably used in deregulated electricity market for conges- tion management. This paper utilizes the difference of LMP across a transmission line to categorize various congestion zones in the system. After the identification of congestion zones, distributed generation is optimally placed in most congestion sensitive zones using LMP difference in order to manage congestion. The performance of the proposed methodology has been tested on the IEEE 14-bus system and IEEE 57-bus system.

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.

Social Energy:Mining Energy From the Society

The inherent nature of energy,i.e.,physicality,sociality and informatization,implies the inevitable and intensive interaction between energy systems and social systems.From this perspective,we define "social energy" as a complex sociotechnical system of energy systems,social systems and the derived artificial virtual systems which characterize the intense intersystem and intra-system interactions.The recent advancement in intelligent technology,including artificial intelligence and machine learning technologies,sensing and communication in Internet of Things technologies,and massive high performance computing and extreme-scale data analytics technologies,enables the possibility of substantial advancement in socio-technical system optimization,scheduling,control and management.In this paper,we provide a discussion on the nature of energy,and then propose the concept and intention of social energy systems for electrical power.A general methodology of establishing and investigating social energy is proposed,which is based on the ACP approach,i.e., "artificial systems"(A), "computational experiments"(C) and "parallel execution"(P),and parallel system methodology.A case study on the University of Denver(DU) campus grid is provided and studied to demonstrate the social energy concept.In the concluding remarks,we discuss the technical pathway,in both social and nature sciences,to social energy,and our vision on its future.

Distribution Locational Marginal Pricing Based Equilibrium Optimization Strategy for Data Center Park with Spatial-temporal Demand-side Resources

This paper proposes a distribution locational marginal pricing(DLMP) based bi-level Stackelberg game framework between the internet service company(ISC) and distribution system operator(DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads(IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

Locational Pricing of Uncertainty Based on Robust Optimization

With the increasing penetration of renewables,power systems have to operate with greater flexibility to address the uncertainties of renewable output.This paper develops an uncertainty locational marginal price(ULMP)mechanism to price these uncertainties.They are denoted as box deviation intervals as suggested by the market participants.The ULMP model solves a robust optimal power flow(OPF)problem to clear market bids,aiming to minimize the system cost as a prerequisite that the reserve margin can address all the relevant uncertainties.The ULMP can be obtained as a by-product of the optimization problem from the Lagrange multipliers.Under the ULMP mechanism,renewables and consumers with uncertainty will make extra payments,and the thermals and financial transmission right(FTR)holders will be compensated.It is further shown that the proposed mechanism has preferable properties,such as social efficiency,budget balance and individual rationality.Numerical tests are conducted on the modified IEEE 5-bus and 118-bus systems to demonstrate the merits and applicability of the proposed mechanism.

P2P Energy Trading Considering Prosumer Preference and System Risk

As an important means of mobilizing demand-side resources,peer-to-peer(P2P)energy trading has drawn more and more attention from scholars.This paper constructs a P2P energy trading framework considering prosumers’trading partner preferences(TPPs)and system risk.At first,we build the P2P trading models of prosumers equipped with different distributed energy resources(DERs),and TPP models.Secondly,to solve the established energy trading problem,a fully distributed double-consensus alternating direction method of multipliers(DC-ADMM)is proposed,which can achieve transaction consensus when considering market players’TPPs.Then,a risk-based security constrained economic dispatch(RBSCED)model based on AC power flow is established for the first time,by which a distribution system operator(DSO)checks system security and obtains risk-based locational marginal prices(RLMPs).Moreover,double-regulated price signals related to RLMPs which contain grid utilization prices(GUPs)and DSO’s retail prices realize management of players’transactions.In the end,the proposed method is applied to an IEEE33 bus distribution system.Results show the proposed method effectively reduces system risk and ensures secure operation of system without direct management.

On the Consistency of Renewable-to-Hydrogen Pricing

With the proposal of carbon neutrality goals and hydrogen energy development strategies in various countries,the development and construction of hydrogen supply chains have become important priorities.However,existing research has paid little attention to the hydrogen market and pricing.Therefore,a hydrogen pricing method based on marginal pricing theory is proposed in this paper,which adapts to hydrogen systems with renewable-to-hydrogen as a major source,in the future.A hydrogen energy market is established to define the industrial chain of hydrogen and the hydrogen trading process.The hydrogen market-clearing model is formulated considering a dynamic line pack.Due to its nonconvexity,the model is equivalently converted into mixed-integer second-order cone programming,and the optimality gap is minimized by introducing a penalty term.Based on the clearing solution,the concept and calculation method of the locational marginal hydrogen price(LMHP)are proposed with respect to the locational marginal price(LMP)in electricity markets.Case studies based on a modified Belgium 20-node gas network and Pennsylvania,New Jersey,and Maryland(PJM)market operation data demonstrate the consistency between LMHP and LMP.

Strategic Investment in Transmission and Energy Storage in Electricity Markets

The variability of renewable energy and transmission congestion provide opportunities for arbitrage by merchants in deregulated electricity markets.Merchants strategically invest to maximize their profits.This paper proposes a joint investment framework for renewable energy,transmission lines,and energy storage using the Stackelberg game model.At the upper level,merchants implement investment and operation strategies for deregulated transmission and energy storage to maximize profits.At the middle level,central planners seek to maximize social welfare through investments in centralized renewable energy and energy storage.At the lower level,independent system operators jointly optimize the energy and reserve markets to minimize the total operating costs.Merchants are remunerated through financial rights,which are a settlement method based on locational marginal price.The trilevel optimization problem is reformulated as a tractable single-level one using Karush-Kuhn-Tucker(KKT)conditions and strong duality theory.The interaction between merchants and central planners is studied with an example based on the IEEE 30-bus test system.The assignment of weight coefficients to the corresponding stochastic scenarios can help merchants avoid investment risk,and their effectiveness is verified with the IEEE 118-bus test system.

Calculation Model and Allocation Strategy of Network Usage Charge for Peer-to-peer and Community-based Energy Transaction Market

The emergence of prosumers in distribution systems has enabled competitive electricity markets to transition from traditional hierarchical structures to more decentralized models such as peer-to-peer(P2P)and community-based(CB)energy transaction markets.However,the network usage charge(NUC)that prosumers pay to the electric power utility for network services is not adjusted to suit these energy transactions,which causes a reduction in revenue streams of the utility.In this study,we propose an NUC calculation method for P2P and CB transactions to address holistically economic and technical issues in transactive energy markets and distribution system operations,respectively.Based on the Nash bargaining(NB)theory,we formulate an NB problem for P2P and CB transactions to solve the conflicts of interest among prosumers,where the problem is further decomposed into two convex subproblems of social welfare maximization and payment bargaining.We then build the NUC calculation model by coupling the NB model and AC optimal power flow model.We also employ the Shapley value to allocate the NUC to consumers fairly for the NUC model of CB transactions.Finally,numerical studies on IEEE 15-bus and 123-bus distribution systems demonstrate the effectiveness of the proposed NUC calculation method for P2P and CB transactions.

Cost implications of increased solar penetration and time-of-use rate interactions

Electricity-grid operators are facing new challenges in matching load and generation due to increased solar generation and peak-load growth.This paper demonstrates that time-of-use(TOU)rates are an effective method to address these challenges.TOU rates use price differences to incentivize conserving electricity during peak hours and encouraging use during off-peak hours.This strategy is being used across the USA,including in Arizona,California and Hawaii.This analysis used the production-cost model PLEXOS with an hourly resolution to explore how production costs,locational marginal prices and dispatch stacks(type of generation used to meet load)change due to changes in load shapes prompted by TOU rates and with additional solar generation.The modelling focused on implementing TOU rates at three different adoption(response)levels with and without additional solar generation in the Arizona balancing areas within a PLEXOS model.In most cases analysed,implementing TOU rates in Arizona reduced reserve shortages in the Western Interconnect and,in some cases,very substantially.This result is representative of the interactions that happen interconnection-wide,demonstrating the advantage of modelling the entire interconnection.Production costs were decreased by the additional solar generation and the load change from TOU rates,and high response levels reduced the production costs the most for high-solar-generation cases.Load change from TOU rates decreased locational marginal prices for a typical summer day but had inconsistent results on a high-load day.Additional solar generation decreased the usage of combustion turbines,combined cycles and coal-fired generation.

Bidding strategy for wind generation considering conventional generation and transmission constraints

Under the environmental crisis of global warming,more efforts are put in application of low carbon energy,especially low-carbon electricity.Development of wind generation is one potential solution to provide lowcarbon electricity source.This paper researches operation of wind generation in a de-regulated power market.It develops bidding models under two schemes for variable wind generation to analyze the competition among generation companies(GENCOs)considering transmission constraints.The proposed method employs the supply function equilibrium(SFE)for modeling the bidding strategy of GENCOs.The bidding process is solved as a bi-level optimization problem.In the upper level,the profit of an individual GENCO is maximized;while in the lower level,the market clearing process of the independent system operator(ISO)is modeled to minimize the production cost.An intelligent search based on genetic algorithm and Monte Carlo simulation(MCS)is applied to obtain the solution.The PJM five-bus system and the IEEE 118-bus system are used for numerical studies.The results show when wind GENCOs play as strategic bidders to set the price,they can make significant profit uplifts as opposed to playing as a price taker,because the profit gain will outweigh the cost to cover wind uncertainty and reliability issues.However,this may result in an increase in total production cost and the profit of other units,which means consumers need to pay more.Thus,it is necessary to update the existing market architecture and structure considering these pros and cons in order to maintain a healthy competitive market.

Transactive Energy Systems in Active Distribution Networks:A Comprehensive Review

Increasing penetration of distributed energy resources(DERs)introduced by different stakeholders,poses an immense challenge to power network operators.The traditional direct control of local DERs has the risk of violating preferences and privacies of stakeholders.A promising solution for supplydemand coordination is to utilize a transactive energy(TE)based energy management method to indirectly coordinate the local DERs,which enables the distribution-level energy providers,consumers,and prosumers to trade energy with each other through a transactive energy system(TES)trading platform.This paper provides a comprehensive review of a TES and presents a detailed classification from different perspectives,including TES participants,structure,commodity,clearing method,and solution algorithm.The presented detailed component-scale classification can be used as a reference for future TES designs.Finally,two additional market tools,i.e.,penalty mechanism and loss allocation mechanism,are discussed as future focus areas,which can be seen as necessary complements to a TES for ensuring feasibility and fairness of energy trading.