Abstract—Demand response is known as one of the basic components of smart grids that plays an important role in shaping load curves. In most of the prior reports on applying demand response programs, reactive power and load dependency to voltage magnitude have been ignored in distribution grids. In this paper, firstly, we show that the ignorance of the mentioned phenomena can cause a mismatch between the expected value of demand response and the experimental value. This mismatch is known as the demand response mismatch (DRM), which is dependent on some parameters such as load type, load reduction percentage, and network power factor. To overcome this problem, this paper presents a reactive power control model. In addition, a mixed integer nonlinear program is proposed to find the optimal size and location of STATCOMs and the optimal transformer tap settings that minimize the DRM. In this paper, the 16-bus U.K. generic distribution system (UKGDS) is employed to prove the capability of the presented method in DRM reduction. I. INTRODUCTION Smart grids, by using communication, information, and control systems, can increase the quality and reliability of supply in distribution grids among different sources and components of the grids, demand response (DR) programs act as the key components of smart grids. Some of the The work of João P.S. Catalão was supported by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under Projects SAICT-PAC/0004/2015 - POCI-01-0145-FEDER-016434, POCI-01-0145- FEDER-006961, UID/EEA/50014/2013, UID/CEC/50021/2013, and UID/EMS/00151/2013. Also, the research leading to these results has received funding from the EU Seventh Framework Programme FP7/2007- 2013 under grant agreement no. 309048. Amin Shokri Gazafroudi and Juan Manuel Corchado acknowledge the support by the European Commission H2020 MSCA-RISE-2014: Marie Sklodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref. 641794. R. Bajool is with Dept. Electrical and Computer Eng., Isfahan Univ. of Technology, Isfahan, Iran; e-mail: r.bajoul@ec.iut.ac.ir. Miadreza Shafie-khah is with C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal; e-mail: miadreza@ubi.pt. A.S. Gazafroudi is with the Department of Computer Science and Automation, School of Science University of Salamanca, Salamanca, Spain; e-mail: shokri@usal.es. J.M. Corchado is with the Department of Computer Science and Automation, School of Science University of Salamanca, Salamanca, Spain, and also with Osaka Institute of Technology, Osaka, Japan; e-mail: corchado@usal.es. J.P.S. Catalão is with INESC TEC and the Faculty of Engineering of the University of Porto, Porto 4200-465, Portugal, also with C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal, and also with INESC-ID, Instituto Superior Técnico, University of Lisbon, Lisbon 1049- 001, Portugal; e-mail: catalao@ubi.pt. advantages of DR programs are decreasing electricity market-clearing price; increasing the capacity of electricity resources, decreasing investment on transmission infrastructures; and decreasing the blackouts and Market power mitigation [1]. DR programs are divided into two categories: incentive-based programs and price-based programs. In the price-based programs, the distribution operator decreases the system peak load by changing the electricity price under different tariffs. In incentive-based programs, consumers reduce their consumption by receiving incentives from the distribution operator [2]. In [3], the authors have presented a model for designing consumers’ optimal set of consumption under DR programs and considering the operation constraints of distribution networks. This model has been presented as an integrated optimization problem in which maximization of the consumers’ social welfare and reducing the distribution network losses are considered as the objective function. Similar research has been done in [4], and a model for setting consumers’ optimal consumptions is attained using DR price- based programs. Reference [5] has employed giving rewards to carry out incentive-based programs. The amount of reward is set based on the impact of consumers’ load reduction on the voltage of other buses. Moreover, consumers authorized the distribution company to determine the desired level of load reduction based on operation constraints of the distribution network. In [6] and [7], voltage control through the demand side management has been investigated. In [7], this fact has been noticed that there has not been enough research done on the potential of real-time demand response for providing ancillary services. Therefore, in the proposed model, execution of DR programs by a decentralized model called “Grid Explicit Congestion Notification” has been presented to utilize the appliances that have real-time DR capability. It has been shown that implementation the proposed model, guarantees reactive power control in the network with no more need to new compensators, devices for reactive power compensation and voltage control. DR frequency control is another ancillary service that can be provided by the demand side management. In [8], the purpose of running DR is controlling the frequency of grid in the presence of wind generators. DR is presented as a frequency regulator mechanism in microgrids in this algorithm. The main goal of the presented algorithm is achieving minimum load reduction by applying constraints on house appliances. In [9], a decentralized algorithm is presented to utilize appliances to control system frequency. Mitigation of Active and Reactive Demand Response Mismatches through Reactive Power Control Considering Static Load Modeling in Distribution Grids Reza Bajool, Miadreza Shafie-khah, Senior Member, IEEE, Amin Shokri Gazafroudi, Juan Manuel Corchado, João P. S. Catalão, Senior Member, IEEE