The unpredictable nature of the wind generation poses an obstacle to high penetration of wind energy in the electric power systems. Demand response (DR) may be considered as an efficient approach to cope with the energy unbalances caused by the wind power intermittency. However، while high penetration of the wind generation in the power systems leads to reduced market prices but it may result in lowering participation in the DR programs. Fair mechanism for valuation of the DR can increase the demand-side participation which consequently facilitates wind power integration in the power systems. This paper focuses on the economic valuation of the DR according to its potential for mitigating the wind power forecast error in the power system operation. For this purpose، a mathematical model is developed to find the maximum monetary incentive for the DR that the system operator is willing to pay to the DR providers. In the proposed model، DR’s potential in reducing the cost of supplying load as well as its capability in reducing the cost of system reserve، start up and shut down of units، load shedding and wind power spillage، are considered. The results of the proposed valuation method provide valuable information for both the system operator and demand response providers. The proposed method is implemented on a six-bus test system and discussions on results are presented.

The implementation of residential demand response has been a serious challenge for power system operators. The advent of smart grids has provided required communication infrastructures to exchange online signals with smart homes. In this paper, a new model for residential demand response is presented. The model divides the household loads in three categories as uncontrollable, controllable with thermostat, and controllable without thermostats. The proposed demand response model is a mixed real-time pricing (RTP) and incentive-based program capable of reflecting hourly prices while motivating residents to participate in the requested demand side management scheme. The proposed model was validated for a residential complex including 50 smart homes using GAMS software. Results verify the efficiency of the model to shed the peak load and shift the starting time of home appliances appropriately. A sensitivity analysis was conducted to investigate the impact of welfare violation cost and incentive rate on numerical results as well.

Demand response is becoming a promising subject in operation of restructured power systems. As a result, recently more attention is paid to demand response programs. Customers can contribute in operation of power systems by deployment demand response. The growth of customers’ participation in such programs may affect planning of power systems. Therefore, it seems necessary to consider the effects of demand response in planning approaches. In this paper, the impact of demand responsiveness on decision making in generation expansion planning is modeled. Then, avoidance or deferment in installation of new generating units is comprehensively investigated and evaluated by introducing a new simple index. In addition, changes in investment cost and total cost paid by customers are investigated. The effects of demand responsiveness are studied from both the customers’ and generation companies (Gencos) points of view. The proposed model has been applied to modified IEEE 30-bus system and the results are discussed.

Due to the increasing penetration of renewable energies in the power system, the frequency control problem has attracted more attention, while the traditional control methods are not capable of regulating the frequency and securing the stability of the system. In smart grids, demand response as the frequency control tool reduces the dependence on spinning reserve and high cost controllers. In addition to the economic benefits of the demand response control, its high-speed in damping of frequency changes is significant. In this paper, based on intelligent control of demand response and using fuzzy logic and particle swarm optimization algorithm, frequency control of an islanded microgrid is proposed. This approach is capable of continuously balancing the generation and demand in case of natural undetermined variation in the output power of wind generation and photovoltaic systems. In comparison with the related work, the proposed controller improves the settling time, overshoot, and undershoot of the response. Moreover, it resolves the restriction of the energy storage systems and effectively reduces environmental pollution due to diesel operation. The simulation results in an islanded microgrid show the good performance of the proposed intelligent controller for demand response control in comparison with traditional controllers.

Executing demand response programs will be effective when they are accompanied by the considerable cooperation of participants in addition to appropriate planning. Therefore, distribution companies always emphasize on the higher cooperation of participants in demand response programs. However, lack of proper planning may reduce electricity sale or may have negative impacts on the technical indices of the distribution network. Of the issues which have not been considered in most studies related to demand response are the rate of change in load and the impact of distribution network flexibility in reducing the negative impact of load response programs. In this paper, a model has been proposed that can determine the negative effect threshold for participantschr('39') cooperation in demand response programs by considering a wide range of distribution network features that change due to load shifting. Moreover, by considering flexibility in distribution network and introducing new indices, proper prioritization is proposed for applying reward and punishment policies or for determining the necessary technical changes in order to rate of change in load on each feeder. Based on the proposed model, maximum peak power reduction in determined in the RBTS-BUS5 test system. The results confirm the accuracy of the proposed model.

In smart grids paradigm, versatile demand response programs have been devised to accommodate the load flexibilities in optimal scheduling of distribution networks and increasing the techno-economic efficiency of electrification process. The initial models for prioritizing the demand response programs are mainly valid for the case of constant and deterministic load profiles. These models are tackled based on multi-criteria decision making approaches and make use of load’s economic model. This is while, the load demand of end-side consumers are accompanied with uncertainties in their nature. An improper modeling of these uncertainties or excluding of from the developed analytics methods ends in corrupted and unreliable results. To ameliorate the precision of the proposed approach, this study effectively incorporates the load demand uncertainties in prioritizing the demand response programs. The load demand uncertainties are effectively modeled based on normal probability distribution function and represented in the form of adequate number of scenarios. The obtained results are then evaluated based on technique for order preference by similarity to ideal solution (TOPSIS) known as a multi-criteria decision making approach. By this way, the priority of each demand response program is determined. Results demonstrate substantial changes in final ranking of the investigated programs compared to the deterministic load profile in preliminary approaches. This remark approves the significant importance of deploying accurate load demand profiles within prioritizing demand response programs.

Demand Response (DR) is counted as one of the main important components in the smart grids. It means the participation of customers in modifying their consumption patterns at the peak hours in order to reduce costs and improve the network reliability. First, in this paper, different types of the incentive based and price based demand response programs (DRPs) and their subsets are investigated. Then, they are modeled and implemented on the Kermanshah province’s load curve on 22 July 2015. Also, their effects on the price reduction and improving the load curve characteristics and network reliability are presented. Finally, a procedure in order to prioritize DRPs based on the electricity company’s different policies is proposed.

The integration of the distributed energy resources into a single entity can do with virtual power plants. VPP is a cluster of dispatchable and non- dispatchable resource with flexible loads which distributed in allover the grid that aggregated and acts as a unique power plant. Flexible load is able to change the consumption so demand response program is applied to use them to improvement of the power system performance. Virtual power plant generation has uncertainty and it make hard to schedule the VPP. To deal this matter Information gap decision theory hint us to optimal schedule of the VPP. To show the effects of VPP and DRP on power system operation cost a bi-level unit commitment with regard the VPPs and DRP is solved in modified IEEE 24 bus reliability test system. Results in presence of VPP and DRP in both IGDT strategies are compared with disregard VPP and DRP and effectiveness of the proposed model is reflected.

The retail electricity market is the intermediary between end-user customer and the wholesale market to supply customers’ energy. One of the basic tools in interaction between the retailer and the end-user customer is use of demand response programs. In this research, a business-economic model with the aim of maximum interaction between retailer and other electricity market players based on interval optimization and demand response programs is proposed to achieve an optimal decision of retailer in bilateral contracts and the pool electricity market. This proposed model calculates the retailer's profit before and after the implementation of appropriate demand response program and ultimately maximizes the difference between these two values. The results show that by using price-based demand response programs and price elasticity of each customer, maximum participation of customer in reducing peak load of the power grid can be achieved. On the other hand, based on proper modeling of uncertainties such as price elasticity and pool electricity market prices, the retailer's profit will increase with a suitable level of risk. For retailer profit, it can be concluded that based on application of fuzzy and epsilon constraint method, determining an optimal pareto points and retailer profit maximization will occur in real time pricing tariff compared to time of use pricing tariff . From the maximum interaction point of view between the retailer and the end-user customer in the electricity market based on the current research, the preferred policy is the policy of using intelligent methods of price-based demand response programs.

this paper concerns identifying the most impressionable group of customers for participating in demand response programs. this is an important point in energy efficiency policies.
in this paper this aim is achieved by combining data-mining and pattern recognition capabilities with energy management and electricity market concepts.
besides, graph theory and entropy concepts are utilized as influencing tools.
the proposed procedure in this paper is evaluated using a real dataset reated to irish customers electricity consumption and its effectiveness is confirmed.