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A Hybrid Tabu-Enhanced Differential Evolution Meta-heuristic Optimization Technique for Demand Side Management in Smart Grid
Abstract
Energy management is a demanding task which needs efficient scheduling of multiple appliances in a smart home. In this paper, for the scheduling of different appliances in a smart home, we proposed a hybrid of two meta-heuristic techniques. The proposed technique is the hybrid of enhanced differential evolution (EDE) and tabu search algorithm (TS) and it is named tabu EDE (TEDE). This technique is used in a smart home for the scheduling of appliances to reduce peak to average ratio (PAR) for the utility and increase user comfort. For evaluating the performance of TEDE, we produced home energy management system. In this work, we have considered a single home with different smart appliances. These appliances are categorized into three groups: interruptible appliances, non-interruptible and base appliances. We compare a hybrid TEDE with EDE and TS in three parameters: cost, PAR and waiting time. Results show that TEDE performed well in reducing PAR at consumer side as compare to EDE and TS. TEDE also help in increasing user comfort as compared to EDE and TS algorithm. We considered user comfort in terms of waiting time. However, cost is compromised in TEDE but perform well in terms of other parameters: PAR and user comfort. In addition, the relationships between PAR, electricity cost and user comfort are also calculated in all techniques.
... A cuckoo search has a number of advantages over GA and PSO due to its robustness and genericity. Ref [12] applied TLBO (teacher learning-based algorithm) algorithm, GA algorithm, TLGO (teacher learning genetic optimization) algorithm and LP (linear programming) algorithm in order to plan appliances. Flexible devices were classified as "time flexible" and "power flexible" to facilitate efficient power usage in SG. ...
The use of smart grids has enabled a number of planning methods to be developed to optimize energy costs, Peak to Average Ratios (PARs), and consumer satisfaction for load management in industrial, commercial, and domestic sectors. From a technical point of view, achieving optimal outcomes requires Demand Side Management (DSM). In smart grids, utility companies and electric users communicate two-way using digital technology to make a sustainable and economic system. This paper proposes a novel framework within which an Energy Management Controller (EMC) keeps track of each appliance, its operational time, and the costs associated with them. Customers of smart grids are motivated to shift their Off-Peak Hours (OPH) from Peak Hours by presenting incentives in OPH. The metering devices would also save customers costs by preventing load shifting between high- and low-cost periods. In addition, the study proposes the bacterial foraging algorithm and grasshopper optimization algorithm for lessening power price and PAR without compromising user comfort (UC) through appliance planning. The simulation results on a practical test system advocate the high effectiveness and reliable performance of the proposed model.
Internet of Things (IoT) enabled Smart grid (SG) is one of the most advanced technologies, which plays a key role in maintaining a balance between demand and supply by implementing demand response (DR) program. In SG, the main focus of the researchers is on home energy management (HEM) system, which is called demand side management (DSM). Appliance scheduling is an integral part of HEM system as it manages energy demand according to supply, by automatically controlling the appliances and shifting the load from peak to off peak hours. In this paper, the comparative performance of HEM controller embedded with heuristic algorithms; harmony search algorithm (HSA), enhanced differential evolution (EDE) and harmony search differential evolution (HSDE) is evaluated. The integration of renewable energy source (RES) in SG makes the performance of HEM system more efficient. The electricity consumption in peak hours usually creates peaks and increases the cost but integration of RES makes the electricity consumer able to use the appliances in the peak hours.We formulate our problem using multiple knapsack theory that the maximum capacity of the consumer of electricity must be in the range which is bearable for consumer with respect to electricity bill. Feasible regions are defined to validate the formulated problem. Finally, simulation of the proposed techniques is conducted in MATLAB to validate the performance of proposed scheduling algorithms in terms of cost, peak to average ratio and waiting time minimization. OAPA
In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.
Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. 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. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
Demand Side Management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via Home Energy Management (HEM) systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, Evolutionary Algorithms (EAs) (Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA) and Cuckoo search) based DSM model for scheduling the appliances of residential users is presented. The model is simulated in Time of Use (ToU) pricing environment for three cases: (i) traditional homes, (ii) smart homes, and (iii) smart homes with Renewable Energy Sources (RES). Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
Demand Response (DR) programs under the umbrella of Demand Side Management (DSM) tend to involve end users in optimizing their Power Consumption (PC) patterns and offer financial incentives to shift the load at “low-priced” hours. However, users have their own preferences of anticipating the amount of consumed electricity. While installing an Energy Management System (EMS), the user must be assured that this investment gives optimum comfort of bill savings, as well as appliance utility considering Time of Use (ToU). Moreover, there is a difference between desired load distribution and optimally-scheduled load across a 24-h time frame for lowering electricity bills. This difference in load usage timings, if it is beyond the tolerance level of a user, increases frustration. The comfort level is a highly variable phenomenon. An EMS giving optimum comfort to one user may not be able to provide the same level of satisfaction to another who has different preferences regarding electricity bill savings or appliance utility. Under such a diversity of human behaviors, it is difficult to select an EMS for an individual user. In this work, a numeric performance metric,“User Comfort Level (UCL)”isformulatedonthebasisofuserpreferencesoncostsaving,toleranceindelayregardinguse of an appliance and return of investment. The proposed framework (UCL) allows the user to select an EMS optimally that suits his.her preferences well by anticipating electricity bill reduction, tolerable delay in ToU of the appliance and return on investment. Furthermore, an extended literature analysis is conducted demonstrating generic strategies of EMSs. Five major building blocks are discussed and a comparative analysis is presented on the basis of the proposed performance metric.
The large variability in power consumption in electrical power systems (EPS) influences not only growth balance losses and technical losses, but also in some cases reduces energy security. Delayed restoration of power generation, combined with unpredictable weather events leading to the loss of generating power can lead to a situation in which to save the stability of the power system there must be introduced in the system a load power limit or even disconnection of end-user in a given area, which will significantly reduce the comfort of use of energy. This situation can be prevented through either the building of new intervention power units or the aggregated use of new energy technologies, such as distributed network resources (DER), which are part of an intelligent Smart Grid network. Such resources bring together virtual power plants (VPP) and demand side management (DSM). The article presents an alternative decentralized active demand response (DADR) system, that by acting on selected groups of loads reduces peak loads with minimized loss of comfort of energy in use for the end-user. The system operates without any communication. The effectiveness of the proposed solution has been confirmed, outlined in test results obtained by the authors from a developed analytical model, which also contains stochastic algorithms to decrease the negative impact of such DSM systems on the power system (power overshoot and oscillation).
In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.
The generalized vertex cover problem, an extension of classic minimum vertex cover problem, is an important NP-hard combinatorial optimization problem with a wide range of applications. The aim of this paper is to design an efficient local search algorithm with tabu strategy and perturbation mechanism to solve this problem. Firstly, we use tabu strategy to prevent the local search from immediately returning to a previously visited candidate solution and avoiding the cycling problem. Secondly, we propose the flip gain for each vertex, and then the tabu strategy is combined with the flip gain for vertex selecting. Finally, we apply a simple perturbation mechanism to help the search to escape from deep local optima and to bring diversification into the search. The experiments are carried on random instances with up to 1000 vertexes and 450,000 edges. The experimental results show that our algorithm performs better than a state-of-art algorithm in terms of both solution quality and computational efficiency in most instances.
This paper presents a software tool that has been developed for optimal configuration of hybrid power systems. These systems can be either interconnected to the main power grid or operated autonomously, and may contain a variety of components, including dispatchable generators (e.g., diesel generators, microturbines, biogas generators), non-dispatchable renewable energy technologies (e.g., wind turbines, photovoltaics), batteries, converters and dump loads. A software tool that optimizes such systems has been developed in MATLAB, using a combination of genetic algorithms and tabu search. The optimal configuration is expressed in terms of minimum cost of electricity (in €/kWh), taking into account operational and component size constraints. The needed input includes weather data (e.g., solar, wind, and temperature time-series), load data, system components data, and general parameters (e.g., project lifetime, discount rate). As a case study, in this paper the tool is used for evaluating an autonomous hybrid power system that includes renewable energy technologies in Chania region, Crete. Moreover, the performance of the tool is investigated for seven additional scenarios of the case study, via sensitivity analysis, studying the effect on the results of the uncertainty of weather and cost data.
This paper focuses on demand-side load management applied to the residential sector. A home automation system controlling household energy is proposed. It is decomposed into three layers: anticipation, reactive and device layers. This paper deals with an anticipation layer that allocates energy by taking into account predicted events. It consists in computing both the starting times of some services and in determining set points of others while satisfying the maximal power constraint. A constraint satisfaction problem formulation has been proposed. Because the complexity is NP-hard, a tabu search is used to solve the problem. It maximizes user comfort and minimizes energy cost. An application example is presented
In the past few years, a number of optimization techniques have been designed for Home Energy Management System (HEMS). In this paper, we evaluated the performance of two heuristic algorithms, i.e., Harmony Search Algorithm (HSA) and Tabu Search (TS) for optimization in residential area. These algorithms are used for efficient scheduling of Smart Appliances (SA) in Smart Homes (SH). Evaluated results show that TS performed better than HSA in achieving our defined goals of cost reduction, improving User Comfort (UC) level and minimization of Peak to Average Ratio (PAR). However, there remains a trade-off between electricity cost and waiting time.
In this paper, a novel error-tolerant iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal battery control and management problems in smart home environments with renewable energy. A main contribution for the iterative ADP algorithm is to implement with the electricity rate, home load demand and renewable energy as quasi-periodic functions, instead of accurate periodic functions, where the discount factor can adaptively be regulated in each iteration to guarantee the convergence of the iterative value function. A new analysis method is developed to guarantee the iterative value function to converge to a finite neighborhood of the optimal performance index function, in spite of the differences of the electricity rate, the home load demand, and the renewable energy in different periods. Neural networks are employed to approximate the iterative value function and control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Numerical results and comparisons are given to illustrate the performance of the developed algorithm.
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
In this paper, we comparatively evaluate the performance of home energy management controller which is designed on the basis of heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO) and ant colony optimization (ACO). In this regard, we introduce a generic architecture for demand side management (DSM) which integrates residential area domain with smart area domain via wide area network. In addition, problem formulation is carried via multiple knapsack problem. For energy pricing, combined model of time of use tariff and inclined block rates is used. Simulation results show that all designed models for energy management act significantly to achieve our objections and proven as a cost-effective solution to increase sustainability of smart grid. GA based energy management controller performs more efficiently than BPSO based energy management controller and ACO based energy management controller in terms of electricity bill reduction, peak to average ratio minimization and user comfort level maximization.
The optimal management of energy generation/consumption in modern distribution systems has gained attention in the smart grid era. This paper presents optimized and coordinated strategies for performing and assessing energy management in multi-microgrid systems. The energy management process is formulated for multi-microgrid systems that simultaneously incorporate several energy generation/consumption units, including different types of distributed generators (DGs), energy storage units, electric vehicles (EVs), and demand response. Due to the probabilistic nature of some loads (e.g., EVs) and generators (e.g., wind turbine and photovoltaic (PV) modules), a novel probabilistic index is defined to measure the success of energy management scenarios in terms of cost minimization. Moreover, by using the new index, common types of energy controllers, such as DGs, storage units, EVs and demand side management are implemented simultaneously and individually, in a system, and the effect of each addition on the defined index and on operational costs is investigated. Finally, the robustness of the process to the load and generation prediction errors is investigated.
From the perspective of global warming mitigation and depletion of energy resources, renewable energy such as wind generation (WG) and photovoltaic generation (PV) are getting attention in distribution systems. Additionally, all-electric apartment houses or residence such as DC smart houses are increasing. However, due to the fluctuating power from renewable energy sources and loads, supply-demand balancing of power system becomes problematic. Smart grid is a solution to this problem. This paper presents a methodology for optimal operation of a smart grid to minimize the interconnection point power flow fluctuation. To achieve the proposed optimal operation, we use distributed controllable loads such as battery and heat pump. By minimizing the interconnection point power flow fluctuation, it is possible to reduce the electric power consumption and the cost of electricity. This system consists of photovoltaic generator, heat pump, battery, solar collector, and load. To verify the effectiveness of the proposed system, results are used in simulation presented.
Study on Power Loss Reduction Considering Load Variation with Large Penetration of Distributed Generation in Smart Grid
- Chang Liu
- Xiangyu Lv
- Li Guo
- Lixia Cai
- Jinxing Jie
- Kuo Su
Liu, Chang, Xiangyu Lv, Li Guo, Lixia Cai, Jinxing Jie, and Kuo Su. "Study on
Power Loss Reduction Considering Load Variation with Large Penetration of Distributed Generation in Smart Grid." In IOP Conference Series: Materials Science
and Engineering, vol. 199, no. 1, p. 012022. IOP Publishing, 2017.