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 optimal 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 solutions 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.

在许多国家中，优化能源消耗是可持续发展问题之一，以改善能源部门的经济和环境指数。本文提出了在客户参与的情况下智能混合能源系统（SHES）的三目标最优性能，以优化重塑日前能源市场中的需求状况。最小化运营成本和排放污染以及最大程度提高客户满意度被视为此问题的目标。考虑了三种类型的需求响应（DR）程序，包括1）减少需求，2）需求转移和3）现场发电程序，以优化客户的电能和热能消耗调度。减少需求计划被视为SHES的储备，而插电式电动汽车（PEV）被视为现场发电计划。能源和储备价格的不确定性使用对数正态分布函数建模。采用改组蛙跳算法（SFLA）来解决产生非支配解的问题。然后，采用模糊法和权重和的混合方法，选择非最优解的最佳解。为了验证上述方法，对五个案例研究进行了调查，结果表明，在可接受的运营成本，排放污染和客户满意度水平下，SHES的最佳调度。能源和储备价格的不确定性使用对数正态分布函数建模。采用改组蛙跳算法（SFLA）来解决产生非支配解的问题。然后，采用模糊方法和权重和的混合方法，选择非最优解的最佳解。为了验证上述方法，对五个案例研究进行了调查，结果表明，在可接受的运营成本，排放污染和客户满意度水平下，SHES的最佳调度。能源和储备价格的不确定性使用对数正态分布函数建模。采用改组蛙跳算法（SFLA）来解决产生非支配解的问题。然后，采用模糊法和权重和的混合方法，选择非最优解的最佳解。为了验证上述方法，对五个案例研究进行了调查，结果表明，在可接受的运营成本，排放污染和客户满意度水平下，SHES的最佳调度。通过模糊法和权重和的混合方法，选择非最优解的最佳解。为了验证上述方法，对五个案例研究进行了调查，结果表明，在可接受的运营成本，排放污染和客户满意度水平下，SHES的最佳调度。通过模糊法和权重和的混合方法，选择非最优解的最佳解。为了验证上述方法，对五个案例研究进行了调查，结果表明，在可接受的运营成本，排放污染和客户满意度水平下，SHES的最佳调度。