Demand Response Analysis of Load Aggregator Considering Energy Storage and User Response Uncertainty

Aiming at the problem that load aggregator (LA) faces greater risk when participating in day-ahead market demand response(DR) due to uncertainty of user response, a day-ahead market decision model of LA considering the uncertainty of response is proposed in this paper. The basic process of LA participating in the day-ahead DR is described in this paper. A DR model considering the uncertainty of response is constructed. Conditional value at risk (CVaR) is used as measurement indexes of decision risk. A benefit-risk portfolio decision model of LA is constructed, and the adaptive particle swarm optimization embedded in Latin hypercube sampling is used to solve the model. The effects of risk preference and response reliability on the benefits and risk losses of LA are discussed. The validity and rationality of the model are verified by an example, which provides a theoretical reference for LA to participate in the operational decision-making of the day-ahead market DR.

As the proportion of new energy in the power grid continues to increase, it brings many challenges to the optimal dispatch of traditional distribution networks. The optimal dispatch strategy of the active distribution network is a key technology that needs to be improved, and the optimization of user energy storage is of great significance. The optimal dispatch strategy of the active distribution network includes many aspects such as source, load, and storage. This paper only considers the optimal charging and discharging strategy of the user's energy storage equipment after the new energy is connected and builds the user's energy storage system cost model, user revenue model, new energy electric field revenue model after energy storage equipment is connected, and energy storage equipment charging and discharging model. The results show that the energy storage optimization proposed in this paper can ensure the interests of the power supply side, the user side, and the power sales company, and is more conducive to mobilizing the three parties to participate in the user load response and energy storage equipment access under time-of-use electricity prices.

The demand response (DR) resources provided by modern industrial users tend to be diversified because of the popularisation of renewable energy. In this context, for the problem of satisfaction and uncertainty in the DR, a load‐scheduling model considering load aggregators (LAs) is presented. In the multi‐level DR scheduling system, the users and aggregator simultaneously participate in the decision; thus, the proposed methodology is a two‐stage optimisation process. The first‐stage optimisation considers the load interruption strategies of complex industrial processes, where a multi‐objective optimisation model is established to coordinate the user benefits and satisfaction. This model is solved by non‐dominated sorting genetic algorithm‐II (NSGA‐II) and the entropy weight double‐base point method to obtain the optional interruptible load (IL) contracts for production processes. The second‐stage optimisation maximises the economic returns of the LA considering the uncertainty of the resource's response, where chance‐constraint programming is applied to solve the problem and select the appropriate IL contract. The effectiveness of the proposed methodology is examined according to the actual industrial production process. The multi‐objective coordination effect of production load‐scheduling is shown in an example. Finally, the effects of joint resource scheduling and different confidence levels on the profit of the aggregator are analysed.

With the development of active distribution network, demand response, as an important adjustable resource, is introduced into the distribution network system to ensure the safe, stable and economic operation of the distribution network system under the combined action of distributed generation, energy storage and other equipment. However, the uncertainty of the external environment in the response process and the uncertainty of the price demand curve make the demand response have greater uncertainty in the current active distribution network system. Based on this, considering the reliable and economical operation of active distribution network, a scheduling optimization model of active distribution network considering demand response uncertainty is proposed in this paper, aiming to minimize the operating cost. This paper focuses on the analysis of two different types of demand response: incentive-based demand response and price-sensitive demand response, whose influence of uncertainty on costs is also considered. And the model is linearized by piecewise linearization. Finally, a modified IEEE33 node example is used to verify that the proposed model can improve the operation superiority of the system to a certain extent.

With the development of the times and the progress of society, the development and change of the demand response potential of China’s residents are facing unprecedented challenges. In today’s big data era, the combination of big data technology and the potential analysis of demand response of China’s residential users has become the inevitable demand of the development of the times. Therefore, in order to better make the demand potential of Chinese residents conform to the development trend of the times, this paper deeply studies the business development trend and status quo of the Internet in the demand analysis and response of residents in recent years through the technology of Internet and big data, and analyzes the potential of demand analysis and response of residents in recent years, A large number of information resources about the demand analysis and response of residential users in the new Internet era are sorted out, and the business fields of residents’ demand analysis and response are re classified. The evaluation model of influencing factors of user demand response behavior is established, and the Monte Carlo simulation calculation method is used for research. It is found that time and price is the main factors influencing the demand response behavior of typical industries. Through the analysis, the accuracy rate of the big data analysis method proposed in this paper reaches 97.3% in studying the potential of residents’ demand response.

A demand response method for an active thermal energy storage air-conditioning system using improved transactive control: On-site experiments

Bilevel load-agent-based distributed coordination decision strategy for aggregators

The future power system will present the typical characteristics of “energy interconnection” and “integration of high-penetration renewable energy”. User-side resources will gradually become important interactive resources to relieve the stress of real-time balance between supply and demand in the system. To fully tap the economic potential of user-side resources, the new energy storage system that consists of loads with a certain adjustment capability on user side is regarded as virtual energy storage (VES). Combining VES with traditional narrow sense energy storage (NSES), the concept of generalized energy storage (GES) is proposed and its characteristics are described based on the response characteristics of NSES and VES. Secondly, considering the uncertainty of VES response, GES response model under the NSES supplementary response strategy is established. On this basis, GES is scheduled by (load aggregator (LA) to participate in the auxiliary service market. The configuration optimization model of NSES with the objective of maximizing the net revenue of LA is established. Finally, under the guidance of the existing electricity auxiliary market policy, the optimization model is simulated. The results show that the NSES configuration strategy increases the revenue of LA while improving the response quality of LA.

Aiming at the problems of high energy consumption cost and insufficient demand response potential of commercial buildings cluster, a comprehensive energy management system for commercial buildings cluster considering distributed generation, energy storage equipment and electrical load is constructed. The optimization model is established with the lowest electricity cost of building operation as the objective function. Meanwhile, through the evaluation and assumption of the demand response potential of various loads in commercial buildings, the demand-price elasticity model under the real-time electricity price mechanism is introduced, and the double-layer optimization algorithm is adopted to optimize the solution of the given model. Through the simulation of the example, the power operating costs of the commercial buildings cluster under three different cases of distributed power generation outputs, as well as whether to participate in demand response are compared. The results show that the energy management scheme and demand response strategy given by the double-layer optimization algorithm can effectively reduce the electricity costs of the commercial buildings cluster and achieve economic operation.

Market strategy options to implement Thailand demand response program policy

Demand response (DR) with the participation of load aggregator (LA) has received extensive attention in recent years due to the increasing energy demand. However, LA has to face the risk that consumers may refuse to be controlled by LA due to the uncertainty of energy consumption on demand side. Therefore, this paper proposes a joint game-theoretical optimization for LAs in DR day-ahead market and intraday market considering the breach of residential consumers. In day-ahead market, LA will compete with other LAs and obtain the optimal bidding amount through a non-cooperative game process, to obtain the maximal self-profit. In intraday market, in order to make up for the breach amount of consumers, DR resource-deficit LAs can purchase resource from DR resource-surplus LAs via Nash bargaining process. Basically, Nash bargaining model is formulated and solved by translating the optimization problem into two sub-problems. Finally, a case study is performed to show the effectiveness of the proposed DR framework. Simulation results show that the whole profit of all LAs increases 25.9% compared with the scenario where LAs only participate in day-ahead market and will be punished by DR market due to the bidding breach.

The uncertainty of power load is one of the important research directions in demand response uncertainty. Accurate and effective power system load forecasting is an important prerequisite for ensuring the safety, stable operation, and normal production of the power grid. To improve the accuracy of short-term load forecasting in power systems under demand response scenarios, this paper proposes a Transformer load forecasting method that considers demand response potential. Firstly, the change law of response uncertainty with electricity price difference and consumer psychology principles are used to quantify the power demand response results under different probability conditions. Then, Transformer neural networks are used to extract features from user historical load, temperature, electricity price, and other time series data. Finally, a multi-head self-attention mechanism is used to pay attention to the structural relationship between time series data, analyze the importance of input variables at each historical moment on the current load, and achieve high-precision prediction of user load and demand response potential. This article takes industrial users as an example to predict the power load and demand response regulation power of the general component manufacturing industry. Through comparative analysis with actual data, the effectiveness of the proposed method is verified. Compared with other existing methods, the Transformer model that considers demand response performs well in power load forecasting, providing a certain theoretical basis for evaluating the potential of demand response. The subsequent work will study the characteristics of electric, hot, and cold loads and their coupling relationships under the difference of electricity prices, and improve the forecasting performance of user loads and Demand Response Regulation power, so as to reduce the power generation and operation costs of the grid.

In the reform of the electricity market, along with the gradual opening of the electricity sales side as well as the increase in the proportion of residential electricity consumption, the user load of the demand side has become an essential resource for demand response (DR). To efficiently utilize the residential load resources, new market participants, such as load aggregator (LA) have emerged. First, the basic concept of load aggregator is introduced in this paper, the origin and definition of LA is studied, and the classification of aggregated resources and the current situation of LA operation in some countries are presented. Then the article analyzes the market operation mode of LA and the uncertainty of LA in operation in detail, including the LA service on the user side, transaction mode and hierarchical structure associated with the operation, the uncertainty classification analysis, and associated strategies to address the problem. The LA load integration method and the scheduling control strategy are discussed. Finally, suggestions and ideas on the future research direction are proposed.

Energy storage technology is to achieve large-scale access to renewable energy sources; the key technology for improving efficiency, safety and economy of power systems is also to increase the ratio of clean energy to power generation, and effective means of promoting haze governance. By the end of 2015, the total installed capacity of the global energy storage equipment was about 167 GW, about 2.9% of the world's total installed power; the energy storage equipment in China is 22.8 GW, about 1.7% of the total installed power of the country. By 2050, China's energy storage equipment will reach 200 GW; the market size will reach more than 2 trillion RMB. The existing energy storage technologies include pumped storage, compressed air energy storage, flywheel energy storage, superconducting storage, lead-acid batteries, lithium batteries, sodium sulfur batteries, liquid flow batteries and super capacitors. Different energy storage technologies are applicable to different applications and fields, depending on system power and discharge time, the main application areas of energy storage technology can be divided into three parts: energy management, power bridging and power quality management. Future energy storage market development will focus on distributed energy storage, distributed photovoltaic PV + energy storage, Micro grid, distribution network side and user side and other fields. In recent years, China's energy storage industry has accelerated the pace of development in terms of project planning, policy support and capacity layout, in the next few years, with the rapid development of renewable energy industry, the energy storage market will also enjoy rapid growth. However, the energy storage industry in this country is still in the initial stage of development, and it is mainly based on demonstration and application, the commercial application of energy storage faces the high cost of storage and the imperfect market of power exchange. Energy storage technology route is not mature, lack of energy storage, effective price and effective incentives are both opportunities and challenges.

Residential side flexible load participation in demand response (DR) has been widely used in recent years. However, there are uncertainties in the response willingness and response volume of residential customers, which have a non‐negligible impact on the security and economic dispatch of the power sector. This paper analyzes the uncertainty factors in the demand response process of residential users from the equipment load layer–user layer, establishes for the first time a refined model of residential users' participation in DR from the perspectives of participation uncertainty and response uncertainty, and further constructs a grid optimal dispatch model considering DR uncertainty. The experimental simulation results show that the model established in this paper is able to adjust the operation strategy according to the requirements of user comfort, enhance the enthusiasm of users to participate in DR, and at the same time reduce the dispatch security risk and economic loss caused by the uncertainty of residents' DR on the power grid.