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Swarm Intelligence Based Home Energy Management Controller Under Dynamic Pricing Scheme
Abstract
Energy management controller (EMC) is widely adopted for residential load management in smart grid (SG). Its main focus is on minimizing cost with minimal consumer interaction. EMC effectiveness is improved in the context of demand response (DR) program. In the era of demand side management (DSM) EMC plays a significant role in residential energy management by curtailing the peak to average ratio (PAR) and electricity bill. In this research work, we are reducing the electricity cost and PAR by maximizing the user comfort. In our research work, we consider a smart home with three smart appliances and schedule these appliances energy consumption and user comfort by using heuristic techniques: binary particle swarm optimization (BPSO) and firefly algorithm (FA). Simulations are conducted by utilizing the day-ahead real time pricing (DA-RTP). Simulation results depict that firefly algorithm (FA) has better performance as compared to BPSO in term of minimizing cost and PAR, while also maximizing user satisfaction level. A trade-off analysis between user satisfaction and energy consumption cost is demonstrated in simulations.
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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 end-users 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 optimization-based algorithm in the HEMS instead of a simple rule-based algorithm under different pricing schemes.
Electricity usage at electricity rush hour (peak hour) may vary from each and every service area such as industrial area, commercial area and residential area. Equalizing the power consumption in industry may lead to the utilization of power in other service areas in an efficient way. Although industries have comparably lesser number of power consuming device types than other service areas the power consumption is quite high. To meet the demands rising in the industry, shiftable loads (devices) can be redistributed equally to all the working time slots based on the average power utilization. It can be done in a flexible manner by shaping the loads using Demand Side Management (DSM) technique in Smart Grid. The main objective is to minimize the power utilization during the electricity rush hour by effectively distributing the power available during off-peak hour. Evolutionary algorithm can be well adapted to problems where optimization is the core criteria. Any maximization or minimization problem can be solved efficiently using evolutionary algorithm. Hence, to obtain the optimized fitness function of load redistribution in industry Genetic Algorithm in Demand Side Management (GA-DSM) is chosen and it has benefited with an overall reduction of 21.91% which is very remarkable. In addition to this the evaluation of the fitness function using GA-DSM is carried out in other two industrial dataset models (steel plant and wind power plant) which is unavailable so far in the literature.
In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home.
The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users’ energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.
In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.
Smart grid is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies has enabled the successful implementation of smart grid, which aims at provision of demand side management mechanisms, such as demand response. In this paper, we propose residential load scheduling model for demand side management. It is assumed that electric prices are announced on day-ahead basis. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem, and an optimal schedule is achieved by solving the minimization problem. Simulation results validate that teacher learning based optimization performs better as compared to genetic algorithm, showing comparable results with linear programming with less computational efforts. TLBO is able to obtain the desired trade-off between consumer electric bill and user discomfort.
A smart grid is a modernized form of the traditional grid. Smart grid benefits both, consumer and energy services provider. Demand side management is one of the key component of smart grid to fulfill consumersi electricity demands in an efficient manner. It helps consumers to manage their load in an effective way to reduce their electricity bill. In this paper, we design a home energy management controller based on three heuristic techniques: teaching learning based optimization, binary particle swarm optimization and enhanced differential evaluation. The major objective of designing this controller is to minimize consumers electricity bill while maximizing consumers satisfaction. Simulation results show that TLBO achieved maximum user satisfaction at minimum cost and peak to average ratio. A trade-off analysis between user satisfaction and energy consumption cost is demonstrated in simulation results.
In this paper, a new approach has been explained for the demand side management strategy. The problem of load shifting in order to minimize the peak demand and reduce the utility cost has been approached in hour wise manner, starting from the first hour till the last hour of the day. To fulfill the objective of load shifting through minimization problem, particle swarm optimization (PSO) algorithm has been modified foe the DSM problem and implemented in three area loads of smart grid i.e. residential, commercial and industrial.
With the arrival of smart grid era and the advent of advanced communication and information infrastructures, bidirectional communication, advanced metering infrastructure, energy storage systems and home area networks would revolutionize the patterns of electricity usage and energy conservation at the consumption premises. Coupled with the emergence of vehicle-to-grid technologies and massive distributed renewable energy, there is a profound transition for the energy management pattern from the conventional centralized infrastructure towards the autonomous responsive demand and cyber-physical energy systems with renewable and stored energy sources. Under the sustainable smart grid paradigm, the smart house with its home energy management system (HEMS) plays an important role to improve the efficiency, economics, reliability, and energy conservation for distribution systems. In this paper, a brief overview on the architecture and functional modules of smart HEMS is presented. Then, the advanced HEMS infrastructures and home appliances in smart houses are thoroughly analyzed and reviewed. Furthermore, the utilization of various building renewable energy resources in HEMS, including solar, wind, biomass and geothermal energies, is surveyed. Lastly, various home appliance scheduling strategies to reduce the residential electricity cost and improve the energy efficiency from power generation utilities are also investigated.
The presence of energy hubs and the advancement in smart grid technologies have motivated system planners to deploy intelligent multicarrier energy systems entitled “smart energy hub” (S.E. Hub). In this paper, we model S.E. Hub, and propose a modern energy management technique in electricity and natural gas networks based on integrated demand side management (IDSM). In conventional studies, energy consumption is optimized from the perspective of each individual user without considering the interactions with each other. Here, the interaction among S.E. Hubs in IDSM program is formulated as a noncooperative game. The existence and uniqueness of a pure strategy Nash equilibrium (NE) is proved. Additionally, the strategies for each S.E. Hub are determined by proposing a distributed algorithm. We also address the IDSM game in a cloud computing (CC) framework to achieve efficient data processing and information management. Simulations are performed on a grid consisting of ten S.E. Hubs. We compare the CC framework with conventional data processing techniques to evaluate the efficiency of our proposed approach in determining NE. It is also shown that in the NE, the energy cost for each S.E. Hub and the peak-to-average ratio of the electricity demand decrease substantially.
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand.
Critical peak pricing (CPP) is an important means of demand response in electricity markets. As a flexible tariff mechanism, making rational CPP implementation strategies and selecting suitable critical days are crucial to its successful implementation. Based on an analysis of tariff scheme and implementation process of CPP, customer response to CPP is described by a price elasticity matrix of demand, and electricity prices are forecasted by a hybrid electricity price model. Furthermore, a CPP decision model which considers the interests of both customers and load serving entity (LSE) is introduced and then solved by 0-1 integer programming. Numerical results are finally used to prove the effectiveness of the proposed model, which is beneficial to saving customer electricity bills, reducing electricity purchase cost of LSE, hedging against electricity purchasing risk for LSE in wholesale market, and realizing multi-party win.
Wireless sensor networks (WSNs) will play a key role in the extension of the smart grid towards residential premises, and enable various demand and energy management applications. Efficient demand-supply balance and reducing electricity expenses and carbon emissions will be the immediate benefits of these applications. In this paper, we evaluate the performance of an in-home energy management (iHEM) application. The performance of iHEM is compared with an optimization-based residential energy management (OREM) scheme whose objective is to minimize the energy expenses of the consumers. We show that iHEM decreases energy expenses, reduces the contribution of the consumers to the peak load, reduces the carbon emissions of the household, and its savings are close to OREM. On the other hand, iHEM application is more flexible as it allows communication between the controller and the consumer utilizing the wireless sensor home area network (WSHAN). We evaluate the performance of iHEM under the presence of local energy generation capability, prioritized appliances, and for real-time pricing. We show that iHEM reduces the expenses of the consumers for each case. Furthermore, we show that packet delivery ratio, delay, and jitter of the WSHAN improve as the packet size of the monitoring applications, that also utilize the WSHAN, decreases.
Cost and Load Reduction Using Heuristic Algorithms in Smart Grid
- Zafar Iqbal
- Nadeem Javaid
- Imran Mobushir Riaz Khan
- Zahoor Ali Ahmed
- Umar Khan
- Qasim
Iqbal, Zafar, Nadeem Javaid, Mobushir Riaz Khan, Imran Ahmed, Zahoor Ali Khan, and Umar
Qasim. "Cost and Load Reduction Using Heuristic Algorithms in Smart Grid." In Advanced
Information Networking and Applications Workshops (WAINA), 2016 30th International Conference on, pp. 24-30. IEEE, 2016.