Energy & Buildings 216 (2020) 109964 Contents lists available at ScienceDirect Energy & Buildings journal homepage: www.elsevier.com/locate/enbuild Multi-objective performance of smart hybrid energy system with Multi-optimal participation of customers in day-ahead energy market Heydar Chamandoust a,∗ , Ghasem Derakhshan a , Salah Bahramara b a Department of Electrical Engineering, Damavand Branch, Islamic Azad University, Tehran, Iran b Department of Electrical Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran a r t i c l e i n f o Article history: Received 24 November 2019 Revised 1 March 2020 Accepted 12 March 2020 Available online 16 March 2020 Keywords: Multi-objective optimal performance Customer’s participation Shuffled frog leaping algorithm (SFLA) Hybrid approach a b s t r a c t Optimal energy consumption is one of the sustainable development issues in many countries to improve the economic and environmental indices in the energy sector. This paper presents a tri-objective opti- mal performance of a smart hybrid energy system (SHES) in the presence of customer’s participation to optimally reshape the demand profile in the day-ahead energy market. Minimizing the operation costs and the emission pollution as well as maximizing the customer satisfaction level are considered as the objectives of this problem. The three types of demand response (DR) programs consisting of 1) demand curtailment, 2) demand shifting and 3) onsite generation program are considered for optimal scheduling of the electrical and the thermal energy consumption by the customers. The demand curtailment program is considered as the reserve for SHES and the Plug Electric Vehicles (PEVs) are taken into account as the onsite generation program. The uncertainties of energy and reserve prices are modeled using lognormal distribution function. The shuffled frog leaping algorithm (SFLA) is employed to solve the problem from which the non-dominated solutions are generated. Then, the best solution of the non-dominated solu- tions is selected by the hybrid approach of fuzzy method and the weight sum. To validate the mentioned approach, five case studies are investigated and the results demonstrate optimal scheduling of SHES with acceptable levels of operation costs, emission pollution and customer satisfaction. © 2020 Elsevier B.V. All rights reserved. 1. Introduction 1.1. Motivation Concerns about the growing energy consumption in the world and its impacts on the economic and environmental indices are in- creasing. Hence, the optimal energy scheduling of energy systems is a suitable method to supply demand regarding its advantages for the generation and the demand sides [1]. Smart grids (SGs), as modern information technology, in the energy sector can facilitate the energy supply in more impressive processes and it can react to extensive events using real-time information [2]. The bidirectional communication link between the generation-side and the demand- side in the SG provides the participation of demand-side in the op- timal energy problem in the operation time [2,3]. The demand-side scheduling in the SG, which has important impacts on the system’s energy balance, is known as the Demand Response (DR) programs [3]. ∗ Corresponding author. E-mail address: H.chamandoust.ac@gmail.com (H. Chamandoust). The DR has several programs which can change the energy consumption to achieve the optimum demand profile. Actually, DR programs can meet the increasing demand as the generation sources, and they have important role in the demand-side to sup- pling the energy balance in the energy markets. The DR programs are able to transform the customer’s energy consumption based on the energy market price signals and incentives [4,5]. In the incentive-based programs, the customers participate in the mar- ket to reduce their energy consumptions regarding the contingency conditions. These programs include capacity/ancillary services, de- mand bidding, emergency program, interruptible program, and di- rect load control (DLC). The price-based programs are implemented to encourage the customers to manage their demand profile by defining the different tariffs regarding the diverse energy prices. These programs are time-of-use (TOU), real time pricing (RTP), and critical peak pricing (CPP) [6]. More details of DR programs are ex- plained in [6]. The DR programs can be employed in all timescales of the energy management problems as shown in Fig. 1. [7]. The DR programs are not only used to manage the energy consump- tion by shifting and curtailing the loads, but also for supplying the demand of the customers with their energy resources. This supply can be considered as a DR program. Therefore, there are three so- https://doi.org/10.1016/j.enbuild.2020.109964 0378-7788/© 2020 Elsevier B.V. All rights reserved. Downloaded from https://iranpaper.ir https://www.tarjomano.com/order