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Demand Side Management (DSM) plays a significant role in the smart grid to minimize Electricity Cost (EC). Home Energy Management Systems (HEMSs) have recently been studied and proposed explicitly for HEM. In this paper, we propose a novel nature-inspired hybrid Genetic Flower Pollination Algorithm (GFPA) to minimize cost with an affordable delay in appliance scheduling. Our proposed GFPA algorithm combines elements of the Genetic Algorithm (GA) and Flower Pollination Algorithm (FPA) to create a hybrid approach. To assess the effectiveness of the proposed algorithm, we consider a scalable town consisting of 1, 10, 30, and 50 homes, respectively. The proposed solution finds an optimal scheduling pattern that simultaneously minimizes EC and Peak to Average Ratio (PAR) while maximizing User Comfort (UC). We assume that all homes are homogeneous in terms of appliances and power consumption patterns. Simulation results show that our proposed scheme GFPA performs better when applying Critical Peak Pricing (CPP) signal using different Operational Time Intervals (OTIs) and compared with unscheduled, GA, and FPA-based solutions in terms of reducing cost since they achieve on average 98%, 36%, 23%, and 22%, respectively. Similarly, PAR averages 98%, 36%, 59%, and 55%, respectively. While, UC comparing to GA and FPA, are around 88%, 48%, and 63%, respectively. Our proposed scheme achieves better results by applying Real Time Pricing (RTP) signals and different OTIs. As these schemes, i.e., unscheduled, GA, FPA, and GFPA, achieve cost on average 92%, 50%, 29%, and 28%, respectively. While PAR on average 94%, 39%, 62%, and 56%, and UC for GA, FPA, and GFPA on average 98%, 52%, and 49%, respectively. Overall, our proposed GFPA algorithm offers a more effective solution for minimizing EC with an affordable delay in appliance scheduling while considering PAR and UC. [ABSTRACT FROM AUTHOR]

Abstract: This paper examines electricity critical peak pricing (CPP) as a measure for controlling electricity demand at critical peak times. This pricing scheme is designed to facilitate energy conservation not only inside but also outside the home. For this study, we surveyed consumer propensity to leave the home under CPP schemes and analyzed the impact of CPP on consumer cost. The results indicated that higher prices induce a higher rate of going out, while residential conditions such as population density and access to public transportation have a relatively small impact on leaving the home and average energy conservation. However, this is not always the case for aged households with limited mobility; residential conditions have a substantial effect on this segment of the population. Combined with a reduced ability to go out, electricity pricing has a greater negative impact on aged people. These results imply that improving accessibility through transportation development and urban compaction is an effective means of saving electricity alleviating the negative impact of CPP on the aged society of the future. [Copyright &y& Elsevier]

• Developed and validated a TRNSYS-MATLAB model for a model-free self-learning controller. • Peak shifting and cost-saving potential of electrically heated floors were analyzed. • The performance of the controller under different time-based rates was investigated. • More efficient performance of the controller was observed for a critical peak pricing setting. Several utility companies adopted time-based rates to encourage the customers to shift their consumption from high demand hours to those with lower demand. In this regard, the two most commonly used time-based rates are time-of-use (TOU) tariffs and critical peak pricing (CPP). Previously, various heat storage techniques were used in buildings to achieve peak load shifting. In this study, sensible heat storage (by electrically heated floors) is considered to bridge the temporal gap between energy supply and demand. To do so, the performance of energy storage was investigated during hours with lower electricity prices. In this way, the stored heat can be used later during hours when the electricity price is high to maintain thermal comfort. To operate heaters in the concrete slab a model free self-learning control (SLC) system was adopted as it can be widely applied in different houses with minor modification. In this study, a TRNSYS-MATLAB model was developed and validated using experimental data collected during winter 2017-2018. Simulations were conducted to investigate the peak shifting and heating cost-saving potential of the SLC system under TOU and CPP time-based rates of Ontario and Quebec, respectively. The results showed that the SLC system could achieve almost complete peak shifting (TOU: 97.6% and CPP: 99.8%) to reduce the stress on the electrical grid. Moreover, it could decrease the heating cost by about 17% and 21% for TOU and CPP, respectively. The SLC system was found to be more efficient for CPP compared to TOU time-based rates, since the former includes mid-peak hours. Overall, it is beneficial for both the supply as well as demand-side since the SLC system could achieve both peak shifting and heating cost savings. [ABSTRACT FROM AUTHOR]