How Load Aggregators Avoid Risks in Spot Electricity Market:In the Framework of Power Consumption Right Option Contracts

There is uncertainty in the electricity price of spot electricity market,which makes load aggregators undertake price risks for their agent users.In order to allow load aggregators to reduce the spot market price risk,scholars have proposed many solutions,such as improving the declaration decision-making model,signing power mutual insurance contracts,and adding energy storage and mobilizing demand-side resources to respond.In terms of demand side,calling flexible demand-side resources can be considered as a key solution.The user’s power consumption rights(PCRs)are core contents of the demand-side resources.However,there have been few studies on the pricing of PCR contracts and transaction decisions to solve the problem of price forecast deviation and to manage the uncertainty of spot market prices.In addition,in traditional PCR contracts,PCRs are mostly priced using a single price mechanism,that is,the power user is compensated for part of the electricity that was interrupted or reduced in power supply.However,some power users might engage in speculative behaviours under this mechanism.Further,for load aggregators,their price risk avoidance ability has not substantially improved.As a financial derivative,options can solve the above problems.In this article,firstly,the option method is used to build an option pricing optimization model for power consumption right contracts that can calculate the optimal option premium and strike price of option contracts of power consumption rights.Secondly,from the perspective of power users and load aggregators,a simulation model of power consumption right transaction decision-making is constructed.The results of calculation examples show that(1)Under the model in this article,the pricing of option contracts for power consumption rights with better risk aversion capabilities than traditional compensation contracts can be obtained.(2)The decision to sell or purchase the power consumption rights will converge at respective highvalue periods,and option contracts will expedite the process.(3)Option contracts can significantly reduce the loss caused by the uncertainty of spot electricity prices for load aggregators without reducing users’willingness to sell power consumption rights.

Optimal dispatching strategy for residential demand response considering load participation

To facilitate the coordinated and large-scale participation of residential flexible loads in demand response(DR),a load aggregator(LA)can integrate these loads for scheduling.In this study,a residential DR optimization scheduling strategy was formulated considering the participation of flexible loads in DR.First,based on the operational characteristics of flexible loads such as electric vehicles,air conditioners,and dishwashers,their DR participation,the base to calculate the compensation price to users,was determined by considering these loads as virtual energy storage.It was quantified based on the state of virtual energy storage during each time slot.Second,flexible loads were clustered using the K-means algorithm,considering the typical operational and behavioral characteristics as the cluster centroid.Finally,the LA scheduling strategy was implemented by introducing a DR mechanism based on the directrix load.The simulation results demonstrate that the proposed DR approach can effectively reduce peak loads and fill valleys,thereby improving the load management performance.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator(LA).Therefore,this paper regards the flexible user-side resources as a virtual energy storage(VES),and uses the traditional narrow sense energy storage(NSES)to alleviate the uncertainty of VES.In order to further enhance the competitive advantage of LA in electricity market transactions,the operation mechanism of LA in day-ahead and real-time market is analyzed,respectively.Besides,truncated normal distribution is used to simulate the response accuracy of VES,and the response model of NSES is constructed at the same time.Then,the hierarchical market access index(HMAI)is introduced to quantify the risk of LA being eliminated in the market competition.Finally,combined with the priority response strategy of VES and HMAI,the capacity allocation model of NSES is established.As the capacity model is nonlinear,Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it.In order to verify the effectiveness of the model,the data from PJM market in the United States is used for testing.Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response,and the economic benefit of LA can be increased by 52.2%at its maximum.Through the reasonable NSES capacity allocation,LA is encouraged to improve its own resource level,thus forming a virtuous circle of market competition.

Load Static Models for Conservation Voltage Reduction in the Presence of Harmonics

The Conservation Voltage Reduction (CVR) is a technique that aims to achieve the decrease of power consumption as a result of voltage reduction. The customer is supplied with the lowest possible voltage level compatible with the stipulated level by the regulatory agency. International Standards ANSI C84.1-2006 and IEEE std 1250-1995 specify the range of supply voltage to electronics equipment from 0.9 to 1.05 pu of nominal voltage. To analyse the CVR effect in distribution systems with different load characteristics (residential, commercial, industrial or a combination of these), mathematical load models are used. Typically, these equipment/load models are used to analyse load aggregation without any consideration of its nonlinearity characteristics. Aiming to analyse the nonlinear characteristics and its consequences, this paper presents a discussion of the neglected variables as well as the results of a set of measurements of nonlinear loads. Different mathematical models are applied to obtain them for each load. Using these models the load aggregation is evaluated. It is presented that although the models show adequate results for individual loads, the same does not occur for aggregated models if the harmonic contribution is not considered. Consequently, to apply the load model in CVR it is necessary to consider the harmonics presence and the model has to be done using only the fundamental frequency data. The discussion about the causes is done and the models are compared with the measurements.

Estimation of composite load model with aggregate induction motor dynamic load for an isolated hybrid power system

It is well recognized that the voltage stability of a power system is affected by the load model and hence, to effectively analyze the reactive power compensation of an isolated hybrid wind-diesel based power system, the loads need to be considered along with the generators in a transient analysis. This paper gives a detailed mathematical modeling to compute the reactive power response with small voltage perturbation for composite load. The composite load is a combination of the static and dynamic load model. To develop this composite load model, the exponential load is used as a static load model and induction motors （IMs） are used as a dynamic load model. To analyze the dynamics of IM load, the fifth, third and first order model of IM are formulated and compared using differential equations solver in Matlab coding. Since the decentralized areas have many small consumers which may consist large numbers of IMs of small rating, it is not realistic to model either a single large rating unit or all small rating IMs together that are placed in the system. In place of using a single large rating IM, a group of motors are considered and then the aggregate model of IM is developed using the law of energy conservation. This aggregate model is used as a dynamic load model. For different simulation studies, especially in the area of voltage stability with reactive power compensation of an isolated hybrid power system, the transfer function AQ/AV of the composite load is required. The transfer function of the composite load is derived in this paper by successive derivation for the exponential model of static load and for the fifth and third order IM dynamic load model using state space model.

Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building

Modern power grids face the challenge of increasing renewable energy penetration that is stochastic in nature and calls for accurate demand predictions to provide the optimized power supply.Hence,increasing the self-consumption of renewable energy through demand response in households,local communities,and micro-grids is essential and calls for high demand prediction performance at lower levels of demand aggregations to achieve optimal performance.Although many of the recent studies have investigated both macro and micro scale short-term load forecasting(STLF),a comprehensive investigation on the effects of electrical demand aggregation size on STLF is minimal,especially with large sample sizes,where it is essential for optimal sizing of residential micro-grids,demand response markets,and virtual power plants.Hence,this study comprehensively investigates STLF of five aggregation levels(3,10,30,100,and 479)based on a dataset of 479 residential dwellings in Osaka,Japan,with a sample size of(159,47,15,4,and 1)per level,respectively,and investigates the underlying challenges in lower aggregation forecasting.Five deep learning(DL)methods are utilized for STLF and fine-tuned with extensive methodological sensitivity analysis and a variation of early stopping,where a detailed comparative analysis is developed.The test results reveal that a MAPE of(2.47-3.31%)close to country levels can be achieved on the highest aggregation,and below 10%can be sustained at 30 aggregated dwellings.Furthermore,the deep neural network(DNN)achieved the highest performance,followed by the Bi-directional Gated recurrent unit with fully connected layers(Bi-GRU-FCL),which had close to 15%faster training time and 40%fewer learnable parameters.

Congestion management with demand response considering uncertainties of distributed generation outputs and market prices

In recent years,much attention has been devoted to the development and applications of smart grid technologies,with special emphasis on flexible resources such as distributed generations(DGs),energy storages,active loads,and electric vehicles(EVs).Demand response(DR) is expected to be an effective means for accommodating the integration of renewable energy generations and mitigating their power output fluctuations.Despite their potential contributions to power system secure and economic operation,uncoordinated operations of these flexible resources may result in unexpected congestions in the distribution system concerned.In addition,the behaviors and impacts of flexible resources are normally highly uncertain and complex in deregulated electricity market environments.In this context,this paper aims to propose a DR based congestion management strategy for smart distribution systems.The general framework and procedures for distribution congestion management is first presented.A bi-level optimization model for the day-ahead congestion management based on the proposed framework is established.Subsequently,the robust optimization approach is introduced to alleviate negative impacts introduced by the uncertainties of DG power outputs and market prices.The economic efficiency and robustness of the proposed congestion management strategy is demonstrated by an actual 0.4 kV distribution system in Denmark.

Local Energy and Planned Ramping Product Joint Market Based on a Distributed Optimization Method

High penetration of renewable energy generation(REG)in the distribution system increases both the power uncertainty at a given interval and the power variation between two intervals.Reserve markets addressing power uncertainty have been widely investigated.However,there is a lack of market mechanisms regarding the power variation of the load and REGs.This paper thus defines a planned ramping(PR)product to follow the net load variation and extends the local energy market to include the trading of PR products.Players are economically compensated for their PR products.Bidding models of dispatchable generators and flexible load aggregators in the joint market are investigated.To solve the market problem in polynomial time,a distributed market clearing method is developed based on the ADMM algorithm.The joint market is tested on a modified IEEE 33-bus system.It verifies that introducing the PR market can encourage flexible loads to provide more PR service to accommodate the net load variation.As such,the ramping cost of dispatchable generators is reduced by 29.09%in the test case.The planned energy curtailment from REG is also reduced.The computational efficiency of the proposed distributed clearing method is validated by comparing it with a centralized method.