Congestion Management Strategies of Real-Time Market

The high penetration of distributed energy resources (DERs) will significantly challenge the power system operation and control due to their stochastic, intermittent, and fluctuation characteristics. This enhances the difficulty of congestion management of power systems in cross-border electricity market among different regions. For handling this, the Real-Time Market is proposed for balancing capacity trading against congestions. Several strategies for Real-Time Market dealing with congestions are proposed. The strategy of two-stage crossborder markets in Day-ahead, Intra-day and Real Time Market are introduced with the congestion constraints complied. Pre-Contingency strategy is proposed as the advance preparation for the future congestion, and In-Day redispatch is used for regulation. Accordingly, the requirements on facilities considering telemetry and remote control in a fast manner are discussed at last.

The impacts of market reform on the market penetration of natural gas-fired electricity and renewable energy in China

Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by its high regulation price compared with coal power. Market reform is therefore of vital importance to promote the penetration of NGFE. The objective of this study is to analyze the impacts of market reform and the renewable electricity(RE) subsidy policy on the promotion of NGFE and RE. A dynamic game-theoretic model is developed to analyze the interaction among the NG supplier, the power sector and the power grid. Three scenarios are proposed with different policies, including a fixed regulation price of NG and electricity, real-time pricing(RTP) of NG and electricity, and subsidy targeted at RE. The results show that:(1) market reform can sharply decrease the NG price and consequently promote the development of NGFE and RE;(2) subsidy targeted at RE not only promotes the penetration of NGFE and RE, but also increases the utilization ratio of renewables significantly;(3) market reform and the subsidy also enhance consumers’ welfare by reducing their power consumption expenditure.

Resource-specific Orders in European Day-ahead Market Under Different Pricing Rules

This paper addresses two issues that concern the electricity market participants under the European day-ahead market(DAM)framework,namely the feasibility of the attained schedules and the non-confiscation of cleared volumes.To address the first issue,new resource-specific orders,i.e.,thermal orders for thermal generating units,demand response orders for load responsive resources,and energy limited orders for storage resources,are proposed and incorporated in the existing European DAM clearing problem.To address the second issue,two approaches which lead to a non-confiscatory market are analyzed:①discriminatory pricing with side-payments(U.S.paradigm);and②non-discriminatory pricing excluding out-ofmoney orders(European paradigm).A comparison is performed between the two approaches to investigate the most appropriate pricing rule in terms of social welfare,derived revenues for the sellers,and efficiency of the attained results.The proposed model with new resource-specific products is evaluated in a European test system,achieving robust solutions.The feasibility of the attained schedules is demonstrated when using resource-specific orders compared with block orders.Finally,the results indicate the supremacy of discriminatory pricing with side-payments compared with the current European pricing rule.

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.

HEMS-enabled transactive flexibility in real-time operation of three-phase unbalanced distribution systems

This paper proposes a coordinated two-stage real-time market mechanism in an unbalanced distribution system which can utilize flexibility service from home energy management system(HEMS)to alleviate line congestion,voltage violation,and substation-level power imbalance.At the grid level,the distribution system operator(DSO)computes the distribution locational marginal prices(DLMPs)and its energy,loss,congestion,and voltage violation components through comprehensive sensitivity analyses.By using the DLMP components in a firststage optimization problem,the DSO generates two price signals and sends them to HEMS to seek flexibility service.In response to the request of DSO,each home-level HEMS computes a flexibility range by incorporating the prices of DSO in its own optimization problem.Due to future uncertainties,the HEMS optimization problem is modeled as an adaptive dynamic programming(ADP)to minimize the total expected cost and discomfort of the household over a forward-looking horizon.The flexibility range of each HEMS is then used by the DSO in a second-stage optimization problem to determine new optimal dispatch points which ensure the efficient,reliable,and congestionfree operation of the distribution system.Lastly,the second-stage dispatch points are used by each HEMS to constrain its maximum consumption level in a final ADP to assign consumption level of major appliances such as energy storage,heating,ventilation and air-conditioning,and water heater.The proposed method is validated on an IEEE 69-bus system with a large number of regular and HEMS-equipped homes in each phase.

Dual Interval Optimization Based Trading Strategy for ESCO in Day-ahead Market with Bilateral Contracts

Being capable of aggregating multiple energy resources, the energy service company(ESCO) has been regarded as a promising alternative for improving power system flexibility and facilitating the consumption of renewable resources in the electricity market. Considering the uncertain variables in day-ahead(DA) market trading, an ESCO can hardly determine their accurate probability distribution functions. Traditional interval optimization methods are used to process these uncertain variables without specific probability distribution functions.However, the lower and upper bounds of the intervals may change due to extreme weather conditions and other emergent events. Hence, a dual interval optimization based trading strategy(DIOTS) for ESCO in a DA market with bilateral contracts(BCs) is proposed. First, we transfer the dual interval optimization model into a simple model consisting of several interval optimization models. Then, a pessimistic preference ordering method is applied to solve the derived model. Case studies illustrating an actual test system corroborate the validity and the robustness of the proposed model, and also reveal that ECSO is critical in improving power system flexibility and facilitating the ability of absorbing renewable resources.

Two-stage Correction Strategy-based Real-time Dispatch for Economic Operation of Microgrids

Given the different energy rates of multiple types of power generation units,different operation plans affect the economy of microgrids.Limited by load and power generation forecasting technologies,the economic superiority of day-ahead plans is unable to be fully utilized because of the fluctuation of loads and power sources.In this regard,a two-stage correction strategy-based real-time dispatch method for the economic operation of microgrids is proposed.Based on the optimal day-ahead economic operation plan,unbalanced power is validly allocated in two stages in terms of power increment and current power,which maintains the economy of the day-ahead plan.Further,for operating point offset during real-time correction,a rolling dispatch method is introduced to dynamically update the system operation plan.Finally,the results verify the effectiveness of the proposed method.

Impacts of COVID-19 Pandemic on Italian Electricity Demand and Markets

In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining various metrics.The investigation reveals that COVID-19 lockdown caused a drop in load consumption and,consequently,a decrement in day-ahead market prices and an increase in ancillary service prices.

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks （ANN） and weighted least square （WLS） technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices （LMP） in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach pro- posed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland （PJM） interconnec- tion is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

Locational Marginal Pricing Mechanism for Uncertainty Management Based on Improved Multi-ellipsoidal Uncertainty Set

The large-scale integration of renewable energy sources (RESs) brings huge challenges to the power system. A cost-effective reserve deployment and uncertainty pricing mechanism are critical to deal with the uncertainty and variability of RES. To this end, this paper proposes a novel locational marginal pricing mechanism in day-ahead market for managing uncertainties from RES. Firstly, an improved multi-ellipsoidal uncertainty set (IMEUS) considering the temporal correlation and conditional correlation of wind power forecasting is formulated to better capture the uncertainty of wind power. The dimension of each ellipsoidal subset is optimized based on a comprehensive evaluation index to reduce the invalid region without large loss of modeling accuracy, so as to reduce the conservatism. Then, an IMEUS-based robust unit commitment (RUC) model and a robust economic dispatch (RED) model are established for the day-ahead market clearing. Both the reserve cost and ramping constraints are considered in the overall dispatch process. Furthermore, based on the Langrangian function of the RED model, a new locational marginal pricing mechanism is developed. The uncertainty locational marginal price (ULMP) is introduced to charge the RES for its uncertainties and reward the generators who provide reserve to mitigate uncertainties. The new pricing mechanism can provide effective price signals to incentivize the uncertainty management in the day-ahead market. Finally, the effectiveness of the proposed mechanism is verified via numerous simulations on the PJM 5-bus system and IEEE 118-bus system.

A Demand Response System for Wind Power Integration: Greenhouse Gas Mitigation and Reduction of Generator Cycling

A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side,and a generic generation mix of nuclear,hydro,coal,gas and oil-based generators representing the supply side,is investigated using agent-based simulations.The simulation includes a transactive load control in a real-time pricing electricity market.The study investigates the impacts of adding wind power and demand response(DR)on both greenhouse gas(GHG)emissions and generator cycling requirements.The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation.The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors.It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation.However,the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators,and especially on the capacity factor at which they operate.Therefore,if a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes,it will result in higher GHG emissions.The simulations show that DR can yield a small reduction in GHG emissions,but also lead to a smaller increase in emissions in circumstances when,for example,a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes.Nonetheless,DR is shown to enhance overall system operation,particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability.DR reduces the amount of generator cycling by an increased order of magnitude,thereby reducing wear and tear,improving generator efficiency,and avoiding the need for additional operating reserves.The effectiveness of DR for these uses depends on the participation of responsive loads,and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.