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Home energy management system (HEMS) is essential for residential electricity consumers to participate actively in demand response (DR) programs. Dynamic pricing schemes are not sufficiently effective for endusers without utilizing a HEMS for consumption management. In this paper, an intelligent HEMS algorithm is proposed to schedule the consumption of controllable appliances in a smart household. Electric vehicle (EV) and electric water heater (EWH) are incorporated in the HEMS. They are controllable appliances with storage capability. EVs are flexible energy-intensive loads, which can provide advantages of a dispatchable source. It is expected that the penetration of EVs will grow considerably in future. This algorithm is designed for a smart household with a rooftop photovoltaic (PV) system integrated with an energy storage system (ESS). Simulation results are presented under different pricing and DR programs to demonstrate the application of the HEMS and to verify its' effectiveness. Case studies are conducted using real measurements. They consider the household load, the rooftop PV generation forecast and the built-in parameters of controllable appliances as inputs. The results exhibit that the daily household energy cost reduces 29.5%-31.5% by using the proposed optimizationbased algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.

Demand response and distributed generation are key components of power systems. Several challenges are raised at both technical and business model levels for integration of those resources in smart grids and microgrids. The implementation of a distribution network as a test bed can be difficult and not cost-effective; using computational modeling is not sufficient for producing realistic results. Real-time simulation allows us to validate the business model's impact at the technical level. This paper comprises a platform supporting the real-time simulation of a microgrid connected to a larger distribution network. The implemented platform allows us to use both centralized and distributed energy resource management. Using an optimization model for the energy resource operation, a virtual power player manages all the available resources. Then, the simulation platform allows us to technically validate the actual implementation of the requested demand reduction in the scope of demand response programs. The case study has 33 buses, 220 consumers, and 68 distributed generators. It demonstrates the impact of demand response events, also performing resource management in the presence of an energy shortage.

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice

According to importance of demand response programs in smart grids and microgrids, many efforts have been made to change the consumption patterns of the users, and the use of renewable resources has also increased. Significant part of energy consumption belongs to buildings such as residential, commercial, and office buildings. Many buildings are equipping with components that can be used for the participation in demand response programs. The SCADA system plays a key role in this context, which enables the building operator to have control and monitor the consumption and generation. This paper presents a real implementation of an optimization based SCADA system, which employs several controlling and monitoring methods in order to manage the consumption and generation of the building for decision support and participating in demand response events. Since the air conditioning devices are suitable controllable appliances for direct load control demand response, and lighting system as flexible loads for reduction and curtailment, they can play a key role in the scope of demand response programs. In this system, several real controller components manage the consumption of lighting system and air conditioning of the building based on an optimization model. In the case study of the paper, the SCADA system is considered as a player of an aggregation model, which is considered as demand response managing entity, and its performance during demand response events will be surveyed. The obtained results show that with adequate small reduction in the lighting system and air conditioning devices, the electricity customers are able to actively participate in the electricity markets using demand response programs and also for internal efficient use of electricity.