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Comparative study of meta-heuristic approaches towards utilization of home energy management
Abstract
For the purpose of reducing electricity cost and peak to average ratio (PAR), different electricity consumers have now an opportunity to schedule their electrical tasks. In this paper, we comparatively evaluated the performance of three meta-heuristic algorithms for our proposed Home Energy Management system (HEMS) i.e., Enhanced Differential Evolution (EDE), Harmony Search Algorithm (HSA), and Tabu Search (TS). The HEMS presented in this paper is based on Demand Side Management (DSM) and involvement of electricity consumers of a residential area domain to consider major factor of user satisfaction. In order to tackle the pricing for electricity bill calculation, a combined model of time of use (ToU) and critical peak pricing (CPP) is used. To deal with the scheduling of appliances a defined classification of appliances is taken from a part of literature. Simulation results verified that proposed techniques performed competently in achieving the above mentioned objectives.
... In literature, Single-objective Optimization Problems (SOPs) are solved by using heuristic approaches that are, a biologically inspired Genetic Algorithm (GA) [3], metaheuristic such as Differential Evolution (DE) [4], swarm approaches such as Particle Swarm Optimization (PSO) [5], Artificial Bee Colony optimization (ABC) [6], Simulated Annealing (SA) [7], etc. Due to the ability to handle problems with complex characteristics, various evolutionary methods are used in HEMS. Some HEMS have used artificial neural networks [8], evolutionary algorithms such as GAs [9], DE [10], and other metaheuristics [11] for optimized scheduling of devices considering the energy consumption cost, user comfort, time delays, and peak to average ratio (PAR) as objectives. In earlier works, scheduling is done considering cost reduction as the primary objective and other aspects such as user satisfaction, time delays as the constraints, and solved using EAs. ...
The thesis mainly discuss about developing a indicator based MOES named as ISDE+ and its application to the appliance scheduling problem considering two conflicting objectives such as COST and User satisfaction in detail. The Novely lies in developing the IDSE+ algorithm and User satisfaction modelling from the energy disaggregation approach using the publicly available smart meter data.
This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other well-performing evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multi-objective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.
The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA)to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort
IIn this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our realtime rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
As an important part of demand-side management, residential demand response (DR) can not only reduce consumer’s electricity costs, but also improve the stability of power system operation. In this regard, this paper proposes an optimal scheduling model of household appliances for smart home energy management considering DR. The model includes electricity cost, incentive and inconvenience of consumers under time-of-use (TOU) electricity price. Further, this paper discusses the influence of inconvenience weighting factor on total costs. At the same time, the influence of incentive on optimization results is also analyzed. The simulation results show the effectiveness of the proposed model, which can reduce 34.71% of consumer’s total costs. It also illustrates that the total costs will be raised with the increase in inconvenience weighting factor. Thus, consumers will choose whether to participate in DR programs according to their preferences. Moreover, the result demonstrates that incentives are conducive to shifting load and reducing the consumer’s total energy costs. The presented study provides new insight for the applications of residential DR.
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.
In this paper, we propose mathematical optimization models of household energy units to optimally control the major residential energy loads while preserving the user preferences. User comfort is modeled in a simple way which considers appliance class, user preferences and weather conditions. The Wind Driven Optimization (WDO) algorithm with the objective function of comfort maximization along with minimum electricity cost is defined and implemented. On the other hand, for maximum electricity bill and peak reduction, Min-max Regret based Knapsack Problem (K-WDO) algorithm is used. To validate the effectiveness of the proposed algorithms, extensive simulations are conducted for several scenarios. The simulations show that the proposed algorithms provide with the best optimal results with fast convergence rate, as compared to the existing techniques
In smart grid, residential consumers adopt different load scheduling methods to manage their power consumptions with specific objectives. The conventional load scheduling methods aim to maximize the consumption payoff or minimize the consumption cost. In this paper, we introduce a novel concept of cost efficiency-based residential load scheduling framework to improve the economical efficiency of the residential electricity consumption. The cost efficiency is defined as the ratio of consumer's total consumption benefit to its total electricity payment during a certain period. We develop a cost-efficient load scheduling algorithm for the demand-side's day-ahead bidding process and real-time pricing mechanism by using a fractional programing approach. Results show that the proposed scheduling algorithm can effectively reflect and affect user's consumption behavior and achieve the optimal cost-efficient energy consumption profile. For practical consideration, we also take into account the service fee and distributed energy resources (DERs) in our framework, and analyze their impacts on the cost efficiency. Simulation results confirm that the proposed algorithm significantly improves consumer's cost efficiency. It is shown that a higher service fee will decrease the cost efficiency, while the integration of DERs can effectively improve the cost efficiency.
Building energy use associated with non-domestic buildings accounts for approximately 19% of the total UK CO2 emissions. Energy consumption in a non-domestic building is a complex issue due to a wide variety of uses and energy services and therefore the energy demand of individual buildings need to be understood. A pilot study was undertaken to analyse the relationship between the electrical energy demand profiles and user activities for a university building. To gain insight into how the building is used, operated and managed on a daily basis, an online questionnaire was distributed to staff and students as well as interviews conducted with key management personnel. Analysis was performed on the half-hourly electrical demand data for the case-study building to identify key trends and patterns in energy use. The shape and magnitude of energy demand profiles shows a significant trend which does not seem to be strongly connected to occupancy patterns. It was found that the building was mostly controlled by a building management system (BMS) where building users have minimal access to the controls. However, it was interesting to find that the detailed information on the occupancy patterns could help the management team to redesign control strategies for optimum energy performance of the building.
Residential buildings are currently equipped with energy production facilities, e.g., solar rooftops and batteries, which in conjunction with smart meters, can function as smart energy hubs coordinating the loads and the resources in an optimal manner. This paper presents a mathematical model for the optimal energy management of a residential building and proposes a centralized energy management system (CEMS) framework for off-grid operation. The model of each component of the hub is integrated within the CEMS. The optimal decisions are determined in real-time by considering these models with realistic parameter settings and customer preferences. Model predictive control (MPC) is used to adapt the optimal decisions on a receding horizon to account for the deviations in the system inputs. Simulation results are presented to demonstrate the feasibility and effectiveness of the proposed CEMS framework. Results show that the proposed CEMS can reduce the energy cost and energy consumption of the customers by approximately 17% and 8%, respectively, over a day. Using the proposed CEMS, the total charging cycles of the ESS were reduced by more than 50% in a day.
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.
Microgrids are promising in reducing energy consumption and carbon emissions, compared with the current centralised energy generation systems. Smart homes are becoming popular for their lower energy cost and provision of comfort. Flexible energy-consuming household tasks can be scheduled co-ordinately among multiple smart homes to reduce economic cost and CO2. However, the electricity tariff is not always positively correlated with CO2 intensity. In this work, a mixed integer linear programming (MILP) model is proposed to schedule the energy consumption within smart homes using a microgrid system. The daily power consumption tasks are scheduled by coupling environmental and economic sustainability in a multi-objective optimisation with ε-constraint method. The two conflicting objectives are to minimise the daily energy cost and CO2 emissions. Distributed energy resources (DER) operation and electricity-consumption household tasks are scheduled based on electricity tariff, CO2 intensity and electricity task time window. The proposed model is implemented on a smart building of 30 homes under three different price schemes. Electricity tariff and CO2 intensity profiles of the UK are employed for the case study. The Pareto curves for cost and CO2 emissions present the trade-off between the two conflicting objectives.
This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.
With continuously growing energy demand the importance of Demand Response(DR) programs is increasing in Smart Grid. Demand response relies on scheduling of loads under the constraints enforced by the utlity so as to reduce energy cost. Schedulable loads play an important role in such programs. The main aim of Demand Response programs is to minimize the energy cost considering the Maximum Demand (MD) limit and the operating constraints of the demand resources. Binary Particle Swarm Optimization(BPSO) is one of the soft computing methods suitable for addressing commitment problems. This paper suggests a method based on BPSO to solve the load scheduling problem with flexibe and non flexible loads and also having Maximum Demand constraint. The algorithm is validated for a number of loads having different characteristics.
Severe peak rebounds are likely in absence of a system-wide coordination among customers participating in demand response programs. This paper aims to establish a decentralized system-wide framework to coordinate demand response of residential customers in a smart grid. The objective of the framework is to modify system load profile provided that customers' payments are minimized, and their comfort and privacy are preserved. Home load management (HLM) modules, embedded in customers' smart meters are autonomous agents of the framework. The energy service provider iteratively exchanges load information with HLM modules in the hope of achieving his desired load profile. In each iteration, the service provider announces system load profile to HLM modules. The modules, keeping in mind their own financial and comfort constraints, nonsequentially send back load reschedule proposals to modify system load profile. The received proposals are judged whether they improve system load profile or not. HLM modules with accepted proposals apply their proposed schedules. The modified system load profile is then released, and HLM modules' new proposals are gathered and judged. This procedure is repeated to the point at which no further improvement in the system load profile can be experienced. Performance of the framework is shown by applying it to a system with 50 customers.
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.
Demand Side Management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost as well as the per unit cost. The main objective of this paper is to develop a Generic Demand Side Management (G-DSM) model for residential users to reduce Peak-to-Average Ratio (PAR), total energy cost and Waiting Time of Appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on Genetic Algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model both for single and multiple users scenarios.
We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.