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Differential-evolution-earthworm hybrid meta-heuristic optimization technique for home energy management system in smart grid
Abstract
In recent years, advanced technology is increasing rapidly, especially in the field of smart grids. A home energy management systems are implemented in homes for scheduling of power for cost minimization. In this paper, for management of home energy we propose a meta-heuristic technique which is hybrid of existing techniques enhanced differential evolution (EDE) and earthworm optimization algorithm (EWA) and it is named as earthworm EWA (EEDE). Simulations show that EWA performed better in term of reducing cost and EDE performed better in reducing peak to average ratio (PAR). However proposed scheme outperformed in terms of both cost and PAR. For evaluating the performance of proposed technique a home energy system proposed by us. In our work we are considering a single home, consists of many appliances. Appliances are categorized into two groups: Inter-ruptible and un-interruptible. Simulations and results show that both algorithms performed well in terms of reducing costs and PAR. We also measured waiting time to find out user comfort and energy consumption.
... Ghanem et al. [35] combined parts of the artificial bee colony (ABC) with elements from the monarch butterfly optimization (MBO) approach to improve the performance of solving numerical optimization problems. Javaid et al. [36] proposed a hybrid meta-heuristic technique for management of home energy, which integrated enhanced differential evolution (EDE) and earthworm optimization algorithm (EWA) techniques. ...
In view of the slow convergence speed, difficulty of escaping from the local optimum, and difficulty maintaining the stability associated with the basic whale optimization algorithm (WOA), an improved WOA algorithm (REWOA) is proposed based on dual-operation strategy collaboration. Firstly, different evolutionary strategies are integrated into different dimensions of the algorithm structure to improve the convergence accuracy and the randomization operation of the random Gaussian distribution is used to increase the diversity of the population. Secondly, special reinforcements are made to the process involving whales searching for prey to enhance their exclusive exploration or exploitation capabilities, and a new skip step factor is proposed to enhance the optimizer’s ability to escape the local optimum. Finally, an adaptive weight factor is added to improve the stability of the algorithm and maintain a balance between exploration and exploitation. The effectiveness and feasibility of the proposed REWOA are verified with the benchmark functions and different experiments related to the identification of the Hammerstein model.
Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.
The search for powerful optimizers has led to the development of a multitude of metaheuristic algorithms inspired from all areas. This work focuses on the animal kingdom as a source of inspiration and performs an extensive, yet not exhaustive, review of the animal inspired metaheuristics proposed in the 2006–2021 period. The review is organized considering the biological classification of living things, with a breakdown of the simulated behavior mechanisms. The centralized data indicated that 61.6% of the animal-based algorithms are inspired from vertebrates and 38.4% from invertebrates. In addition, an analysis of the mechanisms used to ensure diversity was performed. The results obtained showed that the most frequently used mechanisms belong to the niching category.
Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer’s personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attributes. We apply a spectral relaxation clustering approach to show distinct groups of households within the two most used dynamic pricing schemes: Time-Of-Use and Real-Time Pricing. The results indicate that a more effective design of smart home energy management systems can lead to a better fit between customer and electricity tariff in order to reduce costs, enhance predictability and stability of load and allow for more optimal use of demand flexibility by such systems.
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.
The contributions of Distributed Energy Generation (DEG) and Distributed Energy Storage (DES) for Demand Side Management (DSM) purposes in a smart macrogrid or microgrid cannot be over-emphasised. However, standalone DEG and DES can lead to under-utilisation of energy generation by consumers and financial investments; in grid-connection mode, though, DEG and DES can offer arbitrage opportunities for consumers and utility provider(s). A grid-connected smart microgrid comprising heterogeneous (active and passive) smart consumers, electric vehicles and a large-scale centralised energy storage is considered in this paper. Efficient energy management by each smart entity is carried out by the proposed Microgrid Energy Management Distributed Optimisation Algorithm (MEM-DOA) installed distributively within the network according to consumer type. Each smart consumer optimises its energy consumption and trading for comfort (demand satisfaction) and profit. The proposed model was observed to yield better consumer satisfaction, higher financial savings, and reduced Peak-to-Average-Ratio (PAR) demand on the utility grid. Other associated benefits of the model include reduced investment on peaker plants, grid reliability and environmental benefits. The MEM-DOA also offered participating smart consumers energy and tariff incentives so that passive smart consumers do not benefit more than active smart consumers, as was the case with some previous energy management algorithms.
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 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.
In this work, we propose a Realistic Scheduling Mechanism (RSM) to reduce user frustration and enhance appliance utility by classifying appliances with respective constraints and their time of use effectively. Algorithms are proposed regarding functioning of home appliances. A 24 hour time slot is divided into four logical sub-time slots, each composed of 360 min or 6 h. In these sub-time slots, only desired appliances (with respect to appliance classification) are scheduled to raise appliance utility, restricting power consumption by a dynamically modelled power usage limiter that does not only take the electricity consumer into account but also the electricity supplier. Once appliance, time and power usage limiter modelling is done, we use a nature-inspired heuristic algorithm, Binary Particle Swarm Optimization (BPSO), optimally to form schedules with given constraints representing each sub-time slot. These schedules tend to achieve an equilibrium amongst appliance utility and cost effectiveness. For validation of the proposed RSM, we provide a comparative analysis amongst unscheduled electrical load usage, scheduled directly by BPSO and RSM, reflecting user comfort, which is based upon cost effectiveness and appliance utility.
Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible.
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.
Due to the increasing importance of demand-side resources in the energy internet, this paper proposes an optimal energy flow control strategy for a residential energy local network (RELN), which consists of a small number of households, based on the idea of demand-side management. In particular, a type of optimal and dynamic RELN energy consumption scheduling framework is formulated to minimize the daily total operation cost, while fully considering the output forecast error of renewable energy sources (RESs), the consumption preference of users and the status of the energy storage system. In this framework, we provide a unified appliance model to group various types of appliances, which are connected to the network, into a physical model with the same attributes, and a comprehensive real-time pricing mechanism between the RELN and power distribution company (PDC) is developed based on the generalized total load to guide the electricity consumption behavior of the end-users and to also balance the total residential load. On this basis, a mixed integer programming (MIP) model for dynamic energy management optimization of the RELN is optimized at each decision period and integrated into a model predictive control method to reduce the negative impacts of forecast errors of RESs, thereby realizing an optimal and automatic energy flow control for the RELN. Finally, the effectiveness of the proposed RELN energy flow management framework is verified using several case studies.
Grid modernization through philosophies such as the Smart Grid has the potential to meet increasing demand and integrate new distributed generation resources at the same time. Using advanced communication and computing capabilities, the Smart Grid offers a new avenue of controlling end-user assets, including small units such as home appliances. To enable such evolution, shifting from centralized to decentralized control strategies is required. Effective demand-side management (DSM) and demand response (DR) approaches hold the promise for efficient energy management in homes and neighborhood areas, by enabling the precise control of resources to reduce net demand. However, with such strategies, independently taken decisions can cause undesired effects such as rebound peaks, contingencies, and instabilities in the network. Therefore, the interactions between the energy management actions of multiple households is a challenging issue in the Smart Grid. This paper provides a review of the background of residential load modeling with DR and DSM approaches in a single household and concepts of coordinating mechanisms in a neighborhood area. The objective of this paper is to classify, via comparison, the various coordination structures and techniques from recent research. The results of recent research in the field reveal that the coordination of energy management in multiple households can benefit both the utility (i.e., the service provider) and the customer. The paper concludes with a discussion on the prevalent critical issues in this area.
Although previous studies point to much (untapped) potential for energy efficiency enhancement in industry, empirical research that adapts findings of environmental control to the context of energy management remains widely neglected. Specifically, previous environmental research suggests that the implementation of energy management control systems (EnMCS) could be an effective lever for companies to enhance their production systems and operations towards energy efficiency. Yet, empirical evidence for this theoretical proposition is rather missing; thus, debate continues regarding whether the high investments to set up a comprehensive EnMCS pay off in the long run. Based on a sample of 236 German manufacturing companies, this study combines primary data that captures the configuration of EnMCS with secondary data that was used to calculate energy efficiency. The results provide evidence that the extent of EnMCS implementation positively related to firms’ energy efficiency. Findings from additional moderation analysis suggest that companies might enhance the relationship of EnMCS and energy efficiency performance by establishing a full-time energy manager or by using external energy consulting support.
Heating, Ventilation and Air Conditioning (HVAC) systems represent a significant portion of total residential energy consumption in North America. Programmable thermostats are being used broadly for automatic control of residential HVAC systems while users initialize their everyday schedules and preferences. The main aim of smart grid initiatives such as time-varying prices is to encourage consumers to reduce their consumption during high electricity demand. However, it is usually a hassle to residential customers to manually re-programme their thermostats in response to dynamic electricity prices or environmental conditions that vary over time. In addition, the lack of energy management systems such as thermostats capable of learning autonomously and adapting to users’ schedule and preference changes are major obstacles of existing thermostats in order to save energy and optimally benefit from smart grid initiatives. To address these problems, in this paper an adaptable autonomous energy management solution for residential HVAC systems is presented. Firstly, an autonomous thermostat utilizing a synergy of Supervised Fuzzy Logic Learning (SFLL), wireless sensors capabilities, and dynamic electricity pricing is developed. In the cases that the user may override the decision made by autonomous system, an Adaptive Fuzzy Logic Model (AFLM) is developed in order to detect, learn, and adapt to new user’s preferences. Moreover, to emulate a flexible residential building, a ‘house energy simulator’ equipped with HVAC system, thermostat and smart meter is developed in Matlab-GUI. The results show that the developed autonomous thermostat can adjust the set point temperatures of the day without any interaction from its user while saving energy and cost without jeopardizing user’s thermal comfort. In addition, the results demonstrate that if any change(s) occurs to user’s schedules and preferences, the developed AFLM learns and adapts to new modifications while not ignoring energy conservation aspects.
Real-time energy management within the concepts of home Microgrids (H-MG) systems is crucial for H-MG operational reliability and safe functionality, regardless of simultaneously emanated variations in generation and load demand transients. In this paper, an experimental design and validation of a real-time mutli-period artificial bee colony (MABC) topology type central energy management system (CEMS) for H-MGs in islanding mode is proposed to maximize operational efficiency and minimize operational cost of the H-MG with full degree of freedom in automatically adapt the management problem under variations in the generation and storage resources in real-time as well, suitable for different size and types of generation resources and storage devices with plug-and-play structure, is presented. A self-adapting CEMS offers a control box capability of adapting and optimally operating with any H-MGs structure and integrated types of generation and storage technologies, using a two-way communication between each asset, being a unique inherent feature. This CEMS framework utilizes feature like day-ahead scheduling (DAS) integrated with real-time scheduling (RTS) units, and local energy market (LEM) structure based on Single Side Auction (SSA) to regulate the price of energy in real-time. The proposed system operates based on the data parameterization such as: the available power from renewable energy resources, the amount of non-responsive load demand, and the wholesale offers from generation units and time-wise scheduling for a range of integrated generation and demand units. Experimental validation shows the effectiveness of our proposed EMS with minimum cost margins and plug-and-play capabilities for a H-MG in real-time islanding mode that can be envisioned for hybrid multi-functional smart grid supply chain energy systems with a revolutionary architectures. The better performance of the proposed algorithm is shown in comparison with the mixed integer non-linear programming (MINLP) algorithm, and its effectiveness is experimentally validated over a microgrid test bed. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition.
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.
In this paper, an optimal appliance management strategy proposed is participated in by both energy suppliers and customers. Through mathematical description of the appliance operation, a suitable plan is derived to satisfy both the power limits requested from the power suppliers and the least electricity bills from the end-users. Furthermore, the power suppliers will monitor the behaviours of the energy requirement from the customers. When the demanded electricity is close to the limits, the supplier will send a feedback signal to ask the end-users to stop using some kinds of appliances or postpone their operation for a while. When the demand is too low, a signal will also be sent to the end-users from the supplier to remind that some flexible appliances can be activated or used in order to achieve the “peak load shifting.” Renewable energy is also taken into account in the distribution network as auxiliary energy suppliers. Simulation experiments have been performed to prove the effectiveness of the proposed strategy.
A major challenge to implementing residential demand response is that of aligning the objectives of many households, each of which aims to minimize its payments and maximize its comfort level, while balancing this with the objectives of an aggregator that aims to minimize the cost of electricity purchased in a pooled wholesale market. This paper presents a fast distributed algorithm for aggregating a large number of households with a mixture of discrete and continuous energy levels. A distinctive feature of the method in this paper is that the nonconvex DR problem is decomposed in terms of households as opposed to devices, which allows incorporating more intricate couplings between energy storage devices, appliances and distributed energy resources. The proposed method is a fast distributed algorithm applied to the double smoothed dual function of the adopted DR model. The method is tested on systems with up to 2560 households, each with 10 devices on average. The proposed algorithm is designed to terminate in 60 iterations irrespective of system size, which can be ideal for an on-line version of this problem. Moreover, numerical results show that with minimal parameter tuning, the algorithm exhibits a very similar convergence behavior throughout the studied systems and converges to near-optimal solutions, which corroborates its scalability.
Earthworms are essential animals that aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms in nature. In EWA, the offspring are generated through Reproduction 1 and Reproduction 2 independently, and then, we used weighted sum of all the generated offsprings to get the final earthworm for next generation. Reproduction 1 generates only one offspring by itself that is also special kind of reproduction in nature. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully done by nine improved crossover operators that are an extended version of classical crossover operator used in DE (differential evolution) and GA (genetic algorithm). With the aim of escaping from local optima and improving the search ability of earthworms, the addition of a Cauchy mutation (CM) is made to the EWA method. In order to show the robustness of EWA method, nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed and they are compared between each other through twenty-two high-dimensional benchmarks. The results show that EWA23 (Uniform crossover operator is used in Reproduction 2) performs the best and is further benchmarked on forty-eight functions and an engineering optimization case. The EWA method is able to find the better function values on most benchmark optimization problems than seven other metaheuristic algorithms.
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.