Influence Of Demand Response Research Articles

Power grid load frequency control based on Fractional Order PID combined with pumped storage and battery energy storage

With the increasing proportion of installed renewable energy power generation capacity and the introduction of quantitative energy storage policies for new energy power generation. In view of the influence of intermittent and volatility of distributed generation on the safe of power grid, it is​ particularly important in the future to equip reasonable energy storage to ensure the flexibility of power system. In this paper, a load frequency control (LFC) strategy of hybrid energy storage based on fractional order proportion integral derivative (FOPID) is proposed to solve the frequency modulation problem of battery energy storage system (BESS) and pumped storage station (PSS) participating in the interconnection network. Firstly, the LFC model is established under the nonlinear factors such as regulator dead zone and generation rate constraint on a two-area reheat steam turbine. Secondly, a FOPID controller is designed. Furthermore, the combined LFC simulation is carried out by adding BESS under the two working operations of PSS generating and pumping. Finally, the influence of demand response is considered in the hybrid energy storage control.

An integrated energy system optimization strategy based on particle swarm optimization algorithm

For the sake of increasing the energy efficiency of the city’s energy supply, it is an effective approach to adopt the joint planning of the integrated energy system employing a group of cooling heat and power. However, the coupling matrix in the integrated energy system is quite difficult to formulate. In this paper, a standardized construction method for modeling Energy Hub is proposed. The construction of the model is divided into several steps. In addition, the difficult of solving the coupling matrix of integrated energy system can be availably reduced through this method. Considering the nonlinear problem caused by dispatch factors, this paper proposes a comprehensive energy system optimization strategy by using improved particle swarm optimization algorithm. Finally, using an example of a residential area with cooling, heating and electricity system to verify the effectiveness of the proposed modeling and calculation method . At the same time, the influence of demand response and energy storage on economy during operation is analyzed.

Research on bi-level cooperative robust planning of distributed renewable energy in distribution networks considering demand response and uncertainty

The pressure of climate change and carbon emission reduction has promoted the rapid development of distributed renewable energy. One of the problems is how to connect to the distribution grid economically, efficiently and safely and to achieve the consumption of renewable energy. Meanwhile, the demand response of customers is also an important resource that can be regulated by the distribution grid. Based on this, a bi-level cooperative robust planning model for distributed renewable energy in the distribution grid considering demand response is constructed. In the first layer plan, the position and capacity of distributed renewable energy access is used as decision variables, and the minimum operation cost of the distribution network is minimized. In the second layer plan, the uncertainty of the output of renewable energy is further considered. Aiming at the optimal operating cost of the system and combining with relevant constraints, the lower-level robust optimization model is established by introducing robust optimization, and the optimization iteration is carried out by using SSA algorithm. Finally, the model and the method are verified by an example simulation, and the influence of demand response on the planning results is analyzed. The results show that considering the demand response in the distribution network planning is beneficial to delay the investment cost, enhance the power users’ load flexibility, and effectively avoid load cutting and other problems. The introduction of robust optimized two-level collaborative planning model can effectively promote the consumption of renewable energy and promote the participation of demand response resources in planning.

The influence of demand response on wind-integrated power system considering participation of the demand side

Demand response (DR) can serve as virtual reserve to cope the impact of wind power on system reliability. This paper describes a new approach to investigating the impact of DR in a wind-integrated power system from the perspective of generation adequacy. First, owing to the uncertainty of human behavior, DR cannot be trusted to provide a sufficient reserve. To characterize the associated uncertainty, we use a value function of prospect theory to depict the risk attitude of the customer. Based on this function, we propose a variant Roth-Erev algorithm to characterize the uncertainty of customer participation and measure the available capacity of DR. Second, we introduce the available capacity of DR into operational constraints and construct a DR scheduling model to reduce system operation costs. Finally, based on the uncertainty characterization of DR and a scheduling model, we extend the traditional assessment procedure using Monte-Carlo simulation and propose a novel procedure to evaluate the impact of DR on generation adequacy. Simulation results show that introducing DR can improve the generation adequacy of a wind-integrated power system. The proposed DR scheduling method reduces the operational cost and improves generation adequacy.

Multi-Objective Optimal Sizing for Battery Storage of PV-Based Microgrid with Demand Response

In order to solve the influence of uncertain photovoltaic power (PV) on the stable operation of microgrid (MG), demand response (DR) and battery energy storage system (BESS) need to be introduced simultaneously into the operation optimal scheduling of PV-based microgrid (PV-MG). Therefore, it is of great significance for commercial investment decisions of PV-MG to consider the influence of DR on BESS optimal sizing. Under the peak-valley time-of-use (TOU) price, this paper builds cross-time DR models based on price elasticity matrix. Furthermore, through the introduction of DR and BESS into PV-MG scheduling optimization, the MG investment and benefit model is proposed. Considering the constraint condition such as co-ordination of supply and demand, electricity price elasticity and energy loss of storage system, the improved non-dominated sorting genetic algorithm II (NSGA-II) is utilized to solve the multi-objective optimal allocation model of the BESS with the target of maximum PV consumptive rate and annual net profits. The optimization method was applied to a PV-MG in Guangdong. Through the regulation and control effect of demand response and BESS on load distribution, the uncertainties PV power can be suppressed so as to improve the PV system consumptive level, which is of great guiding significance for BESS optimal sizing under this situation.