Graduate Program in Applied Informatics, University of Fortaleza (UNIFOR), Fortaleza-CE 60811-905, Brazil.
Department of Computing, Federal University of Piauí (UFPI), Teresina-PI 64049-550, Brazil.
Sensors (Basel). 2018 Sep 22;18(10):3207. doi: 10.3390/s18103207.
Demand Response (DR) aims to motivate end consumers to change their energy consumption patterns in response to changes in electricity prices or when the reliability of the electrical power system (EPS) is compromised. Most of the proposals found in the literature only aim at reducing the cost for end consumers. However, this article proposes a home energy management system (HEMS) that aims to schedule the use of each home appliance based on the price of electricity in real-time (RTP) and on the consumer satisfaction/comfort level in order to guarantee the stability and the safety of the EPS. Thus, this paper presents a multi-objective DR optimization model which was formulated as a multi-objective nonlinear programming problem subjected to a set of constraints and was solved using the Non-Dominated Sorted Genetic Algorithm (NSGA-II), in order to determine the scheduling of home appliances for the time horizon. The multi-objective DR optimization model not only to minimize the cost of electricity consumption but also to reduce the level of inconvenience for residential consumers. Moreover, a priori, it is expected to obtain a more uniform demand with fewer peaks in the system and, potentially, achieving a more reliable and safer EPS operation. Thus, the energy management controller (EMC) within the HEMS determines an optimized schedule for each home appliance through the multi-objective DR model presented in this article, and ensures a more economic scenario for end consumers. In this paper, a performance evaluation of HEMS in 15 Brazilian families between 1 January and 31 December 2016 is presented with different electric energy consumption patterns in the cities of Belém-PA, Teresina-PI, Cuiabá-MT, Florianópolis-SC and São Paulo-SP, with three families per city, located in the regions north, northeast, central west, south and the southeast of Brazil, respectively. In addition, a total of 425 home appliances were used in the simulations. The results show that the HEMS achieved reductions in the cost of electricity for all the Scenarios used while minimally affecting the satisfaction/comfort of the end consumers as well as taking into account all the restrictions. The largest reduction in the total cost of electricity occurred for the couple without children, resident in the city of Teresina-PI; with a drop from US$ 99.31 to US$ 90.72 totaling 8.65% savings in the electricity bill. Therefore, the results confirm that the proposed HEMS effectively improves the operating efficiency of home appliances and reduces electricity costs for end consumers.
需求响应（DR）旨在激励终端消费者根据电价变化或电力系统（EPS）可靠性受到影响时改变其能源消费模式。文献中的大多数提案仅旨在降低终端消费者的成本。然而，本文提出了一种家庭能源管理系统（HEMS），旨在根据实时电价（RTP）和消费者满意度/舒适度水平安排每个家电的使用，以保证 EPS 的稳定性和安全性。因此，本文提出了一种多目标需求响应优化模型，该模型被表述为一个多目标非线性规划问题，受到一组约束条件的限制，并使用非支配排序遗传算法（NSGA-II）进行求解，以确定时间范围内家电的调度。多目标需求响应优化模型不仅要最小化用电成本，还要降低居民消费者的不便程度。此外，预期它可以获得更均匀的需求，系统中的峰值更少，并有可能实现更可靠和更安全的 EPS 运行。因此，HEMS 中的能源管理控制器（EMC）通过本文提出的多目标需求响应模型为每个家电确定优化的时间表，并为终端消费者确保更经济的方案。本文介绍了 2016 年 1 月 1 日至 12 月 31 日期间在 15 个巴西家庭中进行的 HEMS 性能评估，这些家庭位于巴西的不同城市，如帕拉州的贝伦、皮奥伊州的特雷西纳、马托格罗索州的库亚巴、南里奥格兰德州的弗洛里亚诺波利斯和圣保罗州的圣保罗，每个城市有三个家庭，分别位于巴西的北部、东北部、中西部、南部和东南部。此外，模拟中总共使用了 425 种家用电器。结果表明，HEMS 在所有使用的方案中都实现了电费的降低，同时对终端消费者的满意度/舒适度的影响最小，同时考虑了所有限制。在没有孩子的夫妇中，电费总成本的降幅最大，他们居住在皮奥伊州的特雷西纳市；电费账单总计节省了 8.65%，从 99.31 美元降至 90.72 美元。因此，结果证实，所提出的 HEMS 有效地提高了家用电器的运行效率，并降低了终端消费者的电费。
