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User satisfaction based home energy management system for smart cities
Abstract
With the advent of smart grid and demand side management techniques, users have opportunity to reduce their electricity cost without compromising their comfort much. In this paper, we evaluate the performance of home energy management system based on user satisfaction. Our objective is to maximize the total user satisfaction within user defined budget. For budget three different scenarios are presented that are; $0.25/day, $0.50/day and $1.00/day. To obtain the desired satisfaction three optimization techniques are used: genetic algorithm (GA), enhanced differential evolution (EDE) algorithm, harmony search algorithm (HSA) and their results are compared in terms of achieved satisfaction.
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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 investigate the use of an stochastic optimisation bio-inspired algorithm, differential evolution, and proposed two fitness (cost) functions that can automatically create an intelligent scheduling for a demand-side management system so that it can use plug-in electric vehicles’s (PEVs) batteries to partially and temporarily fulfil electricity requirements from a set of household units. To do so, we proposed two fitness functions that aim: (a) to use the most amount of energy from the batteries of PEVs while still guaranteeing that they can complete a journey, and (b) to enrich the previous function to reduce peak loads.
Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong 's test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.
Energy Management Systems (EMS) are mainly price driven with minimal consumer interaction. To improve the effectiveness of EMS in the context of demand response, an alternative EMS control framework driven by resident behavior patterns is developed. Using hidden Markov modeling techniques, the EMS detects consumer behavior from real-time aggregate consumption and a pre-built dictionary of reference models. These models capture variations in consumer habits as a function of daily living activity sequence. Following a training period, the system identifies the best fit model which is used to estimate the current state of the resident. When a request to activate a time-shiftable appliance is made, the control agent compares grid signals, user convenience constraints, and the current consumer state estimate to predict the likelihood that the future aggregate load exceeds a consumption threshold during the operating cycle of the requested device. Based on the outcome, the control agent initiates or defers the activation request. Using three consumer reference models, a case study assessing EMS performance with respect to model detection, state estimation, and control as a function of consumer comfort and grid-informed consumption constraints is presented. A tradeoff analysis between comfort, consumption threshold, and appliance activation delay is demonstrated.
Short-term load forecasting (STLF) models are very important for electric industry in the trade of energy. These models have many applications in the day-today operations of electric utility(ies) like energy generation planning, load switching, energy purchasing, infrastructure maintenance and contract evaluation. A large variety of STLF models have been developed which trade-off between forecast accuracy and convergence rate. This paper presents an accurate and fast converging STLF (AFC-STLF) model for industrial applications in a smart grid. In order to improve the forecast accuracy, modifications are devised in two popular techniques: (1) mutual information (MI) based feature selection, and (2) enhanced differential evolution (EDE) algorithm based error minimization. On the other hand, the convergence rate of the overall forecast strategy is enhanced by devising modifications in the heuristic algorithm and in the training process of the artificial neural network (ANN). Simulation results show that accuracy of the newly proposed forecast model is 99.5% with moderate execution time, i.e., we have decreased the average execution of the existing Bi-level forecast strategy by 52.38%.
We propose an efficient harmony search algorithm for charge scheduling of an energy storage system (ESS) with renewable power generators under time-of-use pricing and demand charge policy. We show the superiority of the proposed method by simulating experiments with load and generation profiles of typical residential customers. ESS scheduled by the proposed harmony search algorithm showed improved result over the system optimized using genetic algorithm.
h i g h l i g h t s DSM scheme that could maximize satisfaction within user's budget is presented. Load-satisfaction algorithm using the scheme is developed based on GA. We quantified user satisfaction based on certain rules. We develop cost per unit satisfaction index, k($) that relates budget to satisfaction. The proposed algorithm is effective in offering the maximum satisfaction. a b s t r a c t This paper presents a demand side load management technique that is capable of controlling loads within the residential building in such a way that the user satisfaction is maximized at minimum cost. Load-satisfaction algorithm was developed based on three postulations that allow satisfaction to be quantified. The input data required by the algorithm includes the power ratings of the electrical devices, its time of use, kW h electrical consumption as well as the satisfaction of the user on each electrical appliance at every hour of the day. From the data, the algorithm is able to generate an energy usage pattern, which would give the user maximum satisfaction at a predetermined user-budget. A cost per unit satisfaction index (k) which relates the expenditure of a user to the satisfaction achievable is also derived. To test the applicability of the proposed load-satisfaction management technique, three budget scenarios of $0.25/day, $0.5/day and $1.00/day are performed. The result of each of the scenarios using the proposed techniques is compared to cases in which the electrical appliances are randomly used. The results obtained revealed that the proposed algorithm offered the maximum satisfaction and minimum cost per unit satisfaction for all the scenarios.
With constructions of demonstrative microgrids, the realistic optimal economic dispatch and energy management system are required eagerly. However, most current works usually give some simplifications on the modeling of microgrids. This paper presents an optimal day-ahead scheduling model for a microgrid system with photovoltaic cells, wind turbine units, diesel generators and battery storage systems. The power flow constraints are introduced into the scheduling model in order to show some necessary properties in the low voltage distribution network of microgrids. Besides a hybrid harmony search algorithm with differential evolution (HSDE) approach to the optimization problem is proposed. Some improvements such as the dynamic F and CR, the improved mutation, the additional competition and the discrete difference operation have been integrated into the proposed algorithm in order to obtain the competitive results efficiently. The numerical results for several test microgrids adopting the IEEE 9-bus, IEEE 39-bus and IEEE 57-bus systems to represent their transmission networks are employed to show the effectiveness and validity of the proposed model and algorithm. Not only the normal operation mode but also some typical fault modes are used to verify the proposed approach and the simulations show the competitiveness of the HSDE algorithm.
Thiswork proposes a Differential Evolution (DE) algorithm based energy management system (EMS) to optimize the performance of a microgrid in the presence of renewable energy sources. Various types of distributed generators (DGs), namely Micro Turbine (MT), Phosphoric Acid Fuel Cell (PAFC), Wind Turbine (WT), Photo-voltaic (PV) and battery (batt) are present in the micro-grid. Out of these DGs, WT and PV have a randomized natural behavior. To optimize the micro-grid operation and cover the uncertainty in power generation from the renewable resources, the incentive based demand response programs are incorporated in the EMS. The performance of EMS is optimized by minimizing the operating cost of the micro-grid and by minimizing its pollutant emissions using DE algorithm. The results obtained using DE algorithm are compared with the Particle Swarm Optimization (PSO) based reported results. On comparison the proposed DE algorithm is found to be superior in terms of solution quality and convergence speed.
The increasing penetration of renewable generation in the electric power system has been leading to a higher complexity of grid management due to its inherent intermittency, also with impact on the volatility of electricity prices. Setting the adequate operating reserve levels is one of the main concerns of the System Operator (SO), since the integration of a large share of intermittent generation requires an increased amount of reserve that is needed to balance generation and load. At the same time, the energy consumption in households has been steadily growing, representing a significant untapped savings potential due to consumption waste and load flexibility (i.e., the possibility of time deferring the use of some loads).
An aggregator has been designed to operate as an intermediary between individual energy management systems and the SO/Energy Market, capable of facilitating a load follows supply strategy in a Smart Grid context. The aggregator is aimed at using the flexibility provided by each end-user aggregated into clusters of demand-side resources to satisfy system service requirements, involving lowering or increasing the power requested in each time slot. This contributes to the balance between load and supply and coping with the intermittency of renewable sources, thus offering an attractive alternative to supply side investments in peak and reserve generation.
For this purpose, a multi-objective optimization model has been developed to maximize the aggregator profits, taking into account revenues from the SO/Energy Market and payments to end-user clusters, and minimize the inequity between the amounts of load flexibility provided by the clusters to satisfy grid requests. An approach based on an evolutionary algorithm coupled with a differential evolution algorithm has been designed to deal with this model.
Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances.
Smart scheduling of energy consuming devices in the domestic sector should factor in clean energy generation potential, electricity tariffs, and occupants' behaviour (i.e., interactions with their appliances). The paper presents an Artificial Neural Network/Genetic Algorithm (ANN-GA) smart appliance scheduling approach for optimised energy management in the domestic sector. The proposed approach reduces energy demand in "peak" periods, maximises use of renewable sources (PV and wind turbine), while reducing reliance on grid energy. Comprehensive parameter optimisation has been carried out for both ANN and GA to find the best combinations, resulting in optimum weekly schedules. The proposed artificial intelligence techniques involve a holistic understanding of (near) real-time energy demand and supply within a domestic context to deliver optimised energy usage with minimum computational needs. The solution is stress-tested and demonstrated in a four bedroom house with grid energy usage reduction by 10%, 25%, and 40%, respectively.
Electric Vehicles (EVs) are seen to have some negative impacts on microgrid performance, such as diminishing power quality and efficiency and increasing power losses, voltage variations and even customer energy prices. This paper proposes a new method for evaluating the effect of integrating a large number of EVs on a power system and their impact on the network voltage profile via injecting reactive power into highly-loaded buses. A multi-objective optimization problem is developed to obtain the optimal siting and sizing of charging stations and renewable energy sources (RES). The optimization problem focuses on reducing power losses, improving voltage stability of the system and reducing charging costs of EVs. In order to increase the network load factor some coefficients are introduced. Such coefficients, which depend on wind speed, solar irradiance and hourly peak demand ratio in the load characteristic of day-ahead, help aggregators to charge their EVs in off-peak hours. Differential Evolution (DE) algorithm is used for solving the optimization problem. The performance of the proposed method is evaluated for 69-bus and 94-bus microgrids.
The Differential Evolution (DE) algorithm, introduced by Storn and Price in 1995, has become one of the most efficacious population-based optimization approaches. In this algorithm, use is made of the significant concepts of mutation, crossover, and selection. The tuning control parameters are population size, mutation scaling factor, and crossover rate. Over the last decade, several variants of DE have been presented to improve its performance aspects. In the present paper, we further enhance DE. The population size and mutation scaling factor are taken alone in the tuning process; the crossover rate is treated implicitly in the crossover stage. Five forms for crossover are suggested for the first 100 iterations of the computational algorithm. After this learning period, we pick the form which yields the best value of the objective function in the greatest number of iterations (among the 100). Our algorithm is tested on a total of 47 benchmark functions: 27 traditional functions and 20 special functions chosen from CEC2005 and CEC2013. The results are assessed in terms of the mean and standard deviation of the error, success rate, and average number of function evaluations over successful runs. Convergence characteristics are also investigated. Comparison is made with the original DE and Success-History based Adaptive DE (SHADE) as a state-of-the-art DE algorithm, and the results demonstrate the superiority of the proposed approach for the majority of the functions considered.
The present electric power system structure has lasted for decades; it is still partially proprietary, energy-inefficient, physically and virtually (or cyber) insecure, as well as prone to power transmission congestion and consequent failures. Recent efforts in building a smart grid system have focused on addressing the problems of global warming effects, rising energy-hungry demands, and risks of peak loads. One of the major goals of the new system is to effectively regulate energy usage by utilizing the backbone of the prospectively deployed Automatic Meter Reading (AMR), Advanced Meter Infrastructure (AMI), and Demand Response (DR) programs via the advanced distribution automation and dynamic pricing models. The function of the power grid is no longer a system that only supplies energy to end users, but also allows consumers to contribute their clean energy back to the grid in the future. In the meantime, communications networks in the electric power infrastructure enact critical roles. Intelligent automation proposed in smart grid projects include the Supervisory Control And Data Acquisition/Energy Management Systems (SCADA/EMS) and Phasor Management Units (PMU) in transmission networks, as well as the AMR/AMI associated with field/neighborhood area networks (FAN/NAN) and home area networks (HAN) at the distribution and end-use levels. This article provides an overview of the essentials of the progressive smart grid paradigm and integration of different communications technologies for the legacy power system. Additionally, foreseeable issues and challenges in designing communications networks for the smart grid system are also rigorously deliberated in this paper.