Optimal dispatch of microgrid considering interruptible load

With the increasing penetration of renewable energy sources such as wind and solar power, higher requirements of flexible supply ability for power systems are put forward. In this context, an optimal dispatching method considering two types of demand response is proposed to improve power system flexibility. On the demand side, both the time-of-use (TOU) price and interruptible load are used to guide consumers to actively optimize the load curve, so as to relieve the pressure of the flexible supply of conventional units on the supply side. The demand response model consisting of TOU price and interruptible load is built. Then, taking the minimum cost as the objective function, the optimization model for power system dispatching is solved. Finally, the modified IEEE 30 bus system is taken as a case study. The results show that the consideration of two-type demand response in the optimal dispatching can improve the flexibility while ensuring economy, which verifies the effectiveness of the proposed method.

Large‐scale renewable energy (RE) integration in the power systems is displacing conventional sources of generation, thus reducing the system's inherent inertia and primary frequency response (PFR) capabilities. This reduced response may decline the system's ability to stabilise frequency after inevitable events such as loss of generation or load and thus requires additional PFR. To meet the PFR requirements, fast‐acting resources like energy storage systems (ESS) and demand response (DR) (transferrable and interruptible loads) need to be integrated within the system. In this regard, a novel stochastic security‐constrained unit commitment framework is proposed to assess the potential of ESS and DR to ensure additional PFR requirements and handle post contingency frequency dynamics. Moreover, the proposed methodology analyses the impact of uncertain RE integration on system frequency dynamics using Bayesian inference statistics. Case studies are carried out on 39 bus New England and 118 bus IEEE test systems, incorporating post fault frequency security parameters. Simulation results analyse the PFR contribution of ESS and DR and indicate a substantial reduction in system operating cost and RE curtailment with their integration. The proposed analysis would help system operators to maintain required PFR under high RE penetration, and handle unavoidable contingencies.

Residential demand response is one of the key enabling technologies which plays an important role in managing the load demand of prosumers. However, the load scheduling problem becomes quite challenging due to the involvement of dynamic parameters and renewable energy resources. This work has proposed a bi-level load scheduling mechanism with dynamic electricity pricing integrated with renewable energy and storage system to overcome this problem. The first level involves the formulation of load scheduling and optimization problems as optimal stopping problems with the objective of energy consumption and delay cost minimization. This problem involved the real-time electricity pricing signal, customers load scheduling priority, machine learning (ML) based forecasted load demand, and renewable & storage unit profiles, which is solved using mathematical programming with branch-and-cut & branch-and-bound algorithms. Since the first-level optimization problem is formulated as a stopping problem, the optimal time slots are obtained using a one-step lookahead rule to schedule the load with the ability to handle the uncertainties. The second level is used to further model the load scheduling problem through the dynamic electricity pricing signal. The cost minimization objective function is then solved using the genetic algorithm (GA), where the input parameters are obtained from the first-level optimization solution. Furthermore, the impact of load prioritization in terms of time factor and electricity price is also modeled to allow the end-users to control their load. Analytical and simulation results are conducted using solar-home electricity data, Ausgrid, AUS to validate the proposed model. Results show that the proposed model can handle uncertainties involved in the load scheduling process along with a cost-effective solution in terms of cost and discomfort reduction. Furthermore, the bi-level process ensures cost minimization with end-user satisfaction regarding the dynamic electricity price signal.

Demand Response (DR) has gained importance in the recent years. The prime objective of DR is to reduce the maximum demand. There are various DR schemes. This study focuses on price based DR schemes in which consumer is motivated to change his load demand pattern by pricing based incentives. The paper presents an algorithm to solve the load scheduling problem under variable pricing scheme. To this end the paper formulates the load scheduling problem as a singe stage decision making problem and solves it using Pursuit Algorithm, which maintains an agreeable balance between exploration and exploitation. An algorithm to generate realistic loads is also presented. Simulation experiments were conducted considering different types of loads and pricing.

As an effective measure of demand response programs, interruptible load management has been widely used in electricity markets. This paper addresses the problem of what impacts of interruptible load (IL) contracts will have on the equilibrium outcomes of electricity wholesale markets. First, a market demand model is developed to take considerations of IL contracts. Next, a linear supply function equilibrium model is presented for wholesale markets with IL contracts. The introduction of IL contracts leads to a non-smooth equilibrium problem. To solve this equilibrium problem, a novel solution method is proposed. Finally, numerical examples are presented to verify the effectiveness of the method, in which the uncertainty in market demand is considered to further reflect IL contracts' flexibility. It is shown that with IL contracts, market price and its fluctuation can be lowered. This effect is more obvious when the uncertainty in market demand is relatively large. Furthermore, to achieve the target of lowering market price and its fluctuation, the interruption threshold prices in the IL contracts should be determined carefully.

In Japan, there are many remote islands whose power systems mainly consist of internal combustion generators, and thus, the cost of energy in these areas is expensive. Therefore, renewable energy systems (RESs) such as photovoltaic (PV) and wind generator (WG) have been introduced into the power system in these areas. However, it is hard to determine the output power of these resources. Hence, battery energy storage system (BESS) is introduced. Furthermore, demand response (DR) programs which enable power supplier to control load demand, are attracting attention. DR programs are divided into time-based and incentive-based programs. In this research, real-time pricing (RTP) of time-based and interruptible load (IL) of incentive-based programs are considered as energy management method. RTP is the method that power supplier urges customer to increase or to reduce the consumption by changing electricity price corresponding to the power supply-demand situation, while IL is the customer who consented to reduce or to interrupt the power supply at emergency state of power supply-demand. This research presents the optimal sizing method of PV-WG-BESS system in 100% RES and also indicates the effectiveness of RTP and IL.

Load shedding scheme is utilized to deal with black-outs caused by continuously growing loads. Conventional load shedding scheme repressively shed interruptible loads ignoring the economic profits of them. This paper proposes a smart contract based load shedding scheme in an industrial park. The proposed scheme incentivizes factories in the industrial park to actively shed their interruptible loads by designing a market that maximizes the total social welfare. Furthermore, the load shedding process is implemented using a smart contract to automatically obtain trustable and transparent load shedding results. The modified IEEE 33-bus distribution case proves that the proposed load shedding scheme effectively increases the profits of all participating factories and incentivizes them to shed their interruptible loads.

To ensure the safe and stable operation of the power system in the power market environment, a transaction model of power reserve ancillary services oriented to interruptible loads is constructed. By analyzing the benefits of interruptible users and power grid enterprises, a transaction model of interruptible load is established based on it. After obtaining the value of the minimum cost of power system to purchase interruptible load, the capacity cost, opportunity cost and standby use cost of power reserve are calculated respectively. The objective function of auxiliary power reserve service transaction model with interruptible load is established and the constraint conditions are set, and then the transaction result of auxiliary power reserve service is obtained by solving the objective function of the model. The experimental results show that the model can effectively calculate the power interruption compensation price of user interruptible load, and obtain the interruptible load transaction results according to the rapid capacity quotation, power quotation, etc. At the same time, the model has the lowest cost of reserve capacity and reserve use cost in a certain period of time, and the application effect is good.

With the continuous access of generalized demand- side resources to the electricity market, in order to obtain better load forecasting results, it is necessary to accurately quantify the demand response factor. Based on this, an improved short-term load forecasting model combining Genetic Algorithm (GA) and Long Short-Time Memory neural network (LSTM) considering demand response is proposed. First of all, with the goal of maximizing Load Aggregation (LA) revenue, an optimization model is established for DR sub-signals such as interruptible load, transferable load and energy storage system according to the real-time electricity price, and the GUROBI optimization toolbox is called to solve each DR sub-signal. Then, the demand response signal is introduced into the historical load data of the implementation of LA, and the prediction models that take into account and ignore the demand response are established respectively. Finally, the effectiveness of the GA-LSTM method is proved by comparing the prediction results of ARIMA, BP, LSTM and GA-LSTM models. By taking into account the load forecasting results before and after the demand response, it is concluded that taking into account the demand response signals can improve the forecasting accuracy.

The load characteristic model based on demand response and the load optimization control method in three different scenarios are studied in this paper. Firstly, by decoupling rigid load and flexible load, according to different degree that flexible load participates in demand response, considering the influence of different control variables, three types of load characteristics models of electricity price load, direct control load and interruptible load are constructed. Secondly, based on the established the load characteristic model proposes a reasonable load control strategy optimization control method under the condition of different practical power supply gap constraints. Finally, through three cases of scenarios, we explored the control variables are direct control load and interruptible load control, the objective function is when the deviation between the actual load reduction amount and the power supply gap is the smallest, and the constraint condition considers the demand response contract. The feasibility of the constructed model and the optimization method is verified, which provides a reference for the promotion of user-side demand response in China.

Since electric vehicles (EVs) can operate in two modes, charging and regeneration, the idle EVs can support the system as a reserve resource through vehicle-to-grid (V2G) technology. Suppose, an ideal aggregator who has a great number of EVs joins the grid, and its idle EVs can supply aggregator itself partial power demand. The grid makes a contract for the interruptible loads (ILs) with the aggregator, they agree on the time and capacity that the grid can reduce the aggregator's power supply, meanwhile the aggregator could manage its idle EVs to supply its demand and get the compensation from the grid. Here the grid charge the working and idle EVs during off-peak. The wind speed profile follows a Weibull distribution. Based on 24-hour load forecast curve, an optimal model, whose objective is to minimize the total cost including generating cost, reserve cost, emission cost, loss of load cost and penalty cost for not using the available wind power capacity, is proposed. This model is applied to IEEE 30-bus system. The real load fluctuations, the wind power output and the fault outage of generators are simulated hourly. Then the optimal allocation of generators and minimum total cost are obtained by particle swarm optimization (PSO). Finally, based on Monte Carlo simulation, previous steps are repeated for 1000 times to achieve the final cost and its relationship curves with both the IL capacity and compensational price. (5 pages)

For the uncertainty of wind power, PV and load in virtual power plant (VPP), a two-stage optimal dispatching model considering the participation of demand-side response resources in VPP is developed from two stages: day-ahead and real-time, taking into account the forecast error and customer-side demand response resources. In the day-ahead stage, a day-ahead VPP optimal economic dispatch model is developed using price-based demand response and incentive-based demand response to optimize customer load curves with VPP operating cost as the target. In the real time stage, taking into account the regulation characteristics of the excitation type demand response, the real time power deviation is smoothed together with the internal generator set of VPP to realize the correction of the power output of the equipment before the day. Both phases of the model are modeled on MATLAB software and solved by the CPLEX optimization solver. The simulation results show that the introduction of price-based demand response and incentive-based demand response together can optimize the load profile of users better and reduce the operating cost of VPP; the two-stage optimal scheduling can smooth out the random fluctuations on both sides of the source load in the real-time phase and realize the economic and stable operation of VPP.

Like in most developing countries, meeting the load demand and reduction in transmission grid bottlenecks remains a significant challenge for the power sector in Sierra Leone. In recent years, research attention has shifted to demand response (DR) programs geared towards improving the supply availability and quality of energy markets in developed countries. However, very few studies have discussed the implementation of suitable DR programs for developing countries, especially when utilizing renewable energy (RE) resources. In this paper, using the Freetown’s peak load demand data and the price elasticity concept, the interruptible demand response (DR) program has been considered for maximum demand index (MDI) customers. Economic analysis of the energy consumption, customer incentives, benefits, penalties and the impact on the load demand are analyzed, with optimally designed energy management for grid-integrated battery energy storage system (BESS) and photovoltaic (PV)-hybrid system using the genetic algorithm (GA). Five scenarios are considered to confirm the effectiveness and robustness of the proposed scheme. The results show the economic superiority of the proposed DR program’s approach for both customers and supplier benefits. Moreover, RE inclusion proved to be a practical approach over the project lifespan, compared to the diesel generation alternative.

Price-based demand response for household load management with interval uncertainty

Interruptible load (IL) management as an important component of demand-side management in electricity market affects much the safe and economic operation of the power systems. The cost-benefit of interruptible load participants is analyzed in this paper. The discrete customer category is proposed to indicate the expected compensation for the blackout. This paper presents a model of IL contract, in which the objective function is the minimum compensation cost paid by the power company. The benefits of the power company and the customer are both considered as the restrictions of the model. The actual system examples show the validity of the proposed model.