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In recent years, different Demand Side Management (DSM) techniques have been proposed to involve users in decision making process of Smart Grid (SG). Power consumption pattern of shiftable home appliances is schedule to achieve desired benefits of high User Comfort (UC) and low energy consumption. In this paper, an Energy Management Controller (EMC) is designed by using two meta-heuristic algorithms: Strawberry Algorithm (SBA) and Enhanced Differential Evolution (EDE). The main objectives are electricity bill minimization, reduction in Peak to Average Ratio (PAR) and maximization of UC. However, there always exist a trade-off between cost minimization and UC maximization. Simulation results verify that, SBA perform better then EDE in terms of cost reduction while EDE perform far better than SBA in terms of UC maximization.

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... Genetic algorithm (GA) [33], Harmony search algorithm and Crow search algorithm [36] 4 Minimization of consumer's bill, peak demand and waiting time for appliance execution Multi objective optimization using evolutionary algorithms [2], Multi objective mixed integer linear programming (MOMILP) [35] 5 Maximizing the monetary profit using variant energy sources to reduce fuel costs, and production costs Solution at two stages using Jaya-based optimization [37] 6 Minimization of electricity bill and power consumption and Improved user's comfort Strawberry algorithm (SBA) and Enhanced differential evolution (EDE) [38], Wind-driven optimization (WDO) [28] 7 Improved customer's satisfaction level using micro grids and decentralized distribution system ...

... So that, the precise demand for such loads e.g. air conditioners (AC) and heaters can be specified by controlling their thermostats [38]. EMS specifies the amount of energy to be consumed in their working period. ...

... These seven households are categorized based on the range of monthly electricity consumption defined by Canada Electricity Board (CEB) [41]. The power consumption by these seven households and their category are given in Table 2. Other details of appliances like power rating, category, duration and consumer's preferences are listed in Tables A.1-A.8 [35,37,38]. To assess the efficiency of proposed model a comparative analysis of monthly bill cost for all the seven houses under different pricing schemes is given. ...

In industrial and commercial sectors, numerous countries had successfully implemented the dynamic pricing as a solution to the problem of high power demand in peak hours. But, an extensive use of real-time pricing in the residential electricity sector is hugely missing. In order to boost the efficiency of electricity market by demand response, real-time pricing needs to be implemented into residential sector also. In this paper the proposed algorithm is implemented for residential consumers of different categories with real time pricing data of ComEd, Northern Illinois Power Company, and Alactra Utilities Corporation. The proposed algorithm incorporates single interval and multi interval programming for different power pricing schemes. The proposed algorithm is suggested using metaheuristic optimization techniques viz. cuckoo search (CS), adaptive cuckoo search (ACS) and Hybrid GA-PSO for the optimum scheduling of residential appliances. The objective of this paper is to minimize the monthly electricity bill cost as well as peak demand under uncertain electricity prices. The comparative analysis of optimal solutions obtained by various artificial intelligence techniques validates the high performance of proposed algorithm. It facilitates both the residential consumer and utilities with benefits.

... In single residential home, sixteen number of smart appliances are considered under time of use (TOU) pricing signals. These appliances are further categorized into two: fixed and interruptible and the descriptions of these appliances are found in [12]. ...

... The steps perform during EDE are; initializing initial population; mutation; crossover; evaluating fitness of trial vectors; select trial vector with minimal cost; discover the worst individual in the population. The parameters used in this paper are taken from data used in [12]. ...

... Author in [15], proposes a numerical optimization algorithm inspired by the strawberry plant for resolving continuous multi-variable problems. Runner as well as stolon is the building element for the generation of strawberry plant [12]. The leaf axil form a crawling stalk known as runner and can be propagated from the parent plant. ...

In this paper, we presented JAYA algorithm which is a recently developed scheme that do not need any specific parameter to be adjusted except the known control parameters. To achieve the set of objectives like: electricity bill minimization, peak to average ratio (PAR) reduction, minimum user dissatisfaction , a proposed JAYA energy management controller (JAYA-EMC) for optimization is implemented. In increasing sustainability of smart grid, the simulation results of our proposed scheme is proven to be cost effective solution. In addition, the feasible region (FR) of appliances are calculated which show relationship between energy load, cost and delay.

... The main goals are to reduce the electricity bill, save the peak-to-average ratio, and maximize the uniform communication. The simulation results verify that SBA performs better than EDE in terms of cost saving while EDE performs much better than SBA in terms of UC maximization [26]. ...

It is usually difficult for people to solve real-life problems, although nature has had Its own way of looking at and addressing these challenges for millions of years. This is why these days when people fail to strategize in these circumstances, they turn to nature to solve problems. Thus, it is often difficult for algorithms to arrive at the correct solution to a problem through heuristic processes. On the other hand, these strategies are good for getting close to the required time. One of these algorithms is called the Strawberry Algorithm. Here we propose to study the influence of Strawberry Algorithm roots and runners. In this research, we came up with the best solution to find the roots of a linear equation using one of nature's algorithms, and from these algorithms we find the strawberry algorithm through the Visual Studio 2019 platform. It has been observed that the strawberry algorithm succeeded in finding solutions to the linear equation and was very effective in finding any linear equation and he compared the results of the algorithm with the genetic algorithm in terms of measuring efficiency in time and iteration so that the strawberry algorithm was better than the genetic algorithm.

... In the future, we will improve the estimation models by experimenting with new methods and incorporating cloud computing for estimating purposes in order to obtain more comprehensive results in the future. Researchers and practitioners in [59] and [61] used the Strawberry Plant heuristics approach for software cost estimation and for energy management. In [60,62], Grey Wolf and Bacterial Foraging approaches are used in smart grids for energy management and heterogeneous generalized signcryption to maintain the data integrity for estimation. ...

E ective software cost estimation signi cantly contributes to decision-making. e rising trend of using nature-inspired meta-heuristic algorithms has been seen in software cost estimation problems. e constructive cost model (COCOMO) method is a well-known regression-based algorithmic technique for estimating software costs. e limitation of the COCOMO models is that the values of these coe cients are constant for similar kinds of projects whereas, in reality, these parameters vary from one organization to another organization. erefore, for accurate estimation, it is necessary to ne-tune the coe cients. e research community is now examining deep learning (DL) as a forward-looking solution to improve cost estimation. Although deep learning architectures provide some improvements over existing at technologies, they also have some shortcomings, such as large training delays, over-tting, and under-tting. Deep learning models usually require ne-tuning to a large number of parameters. e meta-heuristic algorithm supports nding a good optimal solution at a reasonable computational cost. Additionally, heuristic approaches allow for the location of an optimum solution. So, it can be used with deep neural networks to minimize training delays. e hybrid of ant colony optimization with BAT (HACO-BA) algorithm is a hybrid optimization technique that combines the most common global optimum search technique for ant colonies (ACO) in association with one of the newest search techniques called the BATalgorithm (BA). is technology supports the solution of multivariable problems and has been applied to the optimization of a large number of engineering problems. is work will perform a twofold assessment of algorithms: (i) comparing the e cacy of ACO, BA, and HACO-BA in optimizing COCOMO II coe cients; and (ii) using HACO-BA algorithms to optimize and improve the deep learning training process. e experimental results show that the hybrid HACO-BA performs better as compared to ACO and BA for tuning COCOMO II. HACO-BA also performs better in the optimization of DNN in terms of execution time and accuracy. e process is executed upto 100 epochs, and the accuracy achieved by the proposed DNN approach is almost 98% while NN achieved accuracy of up to 85% on the same datasets.

... In this regard, they proposed a Genetic Algorithm-based method. Other studies based on metaheuristic algorithms have been presented in the literature in the last few years [46][47][48]. ...

Food needs are increasing day by day, and traditional agricultural methods are not responding efficiently. Moreover, considering other important global challenges such as energy sufficiency and migration crises, the need for sustainable agriculture has become essential. For this, an integrated smart and mechanism-application-based model is proposed in this study. This model consists of three stages. In the first phase (cultivation), the proposed model tried to plant crops in the most optimized way by using an automized algorithmic approach (Sand Cat Swarm Optimization algorithm). In the second stage (control and monitoring), the growing processes of the planted crops was tracked and monitored using Internet of Things (IoT) devices. In the third phase (harvesting), a new method (Reverse Ant Colony Optimization), inspired by the ACO algorithm, was proposed for harvesting by autonomous robots. In the proposed model, the most optimal path was analyzed. This model includes maximum profit, maximum quality, efficient use of resources such as human labor and water, the accurate location for planting each crop, the optimal path for autonomous robots, finding the best time to harvest, and consuming the least power. According to the results, the proposed model performs well compared to many well-known methods in the literature.

... The FOA only focused on the seeding process and ignored the more complex procedures such as competition that can be lead to better results. SBA [28], and RRA are two nature-inspired algorithms that simulate the function of runners and root spreading mechanisms of plants. Some plants such as strawberry expand runners and roots for propagation and searching for water resources and minerals. ...

In this paper, a novel nature-inspired optimization algorithm, hazelnut tree search (HST) is proposed for solving numerical and engineering optimization problems. HST is a multi-agent algorithm that simulates the search process for finding the best hazelnut tree in a forest. The algorithm is composed of three main actuators: growth, fruit scattering, and root spreading. In the growth phase, trees compete with each other on shared resources to grow up and improve their fitness. In the fruit scattering phase, HTS performs exploration by simulating the movement of hazelnuts around the forest with the help of animals and rodents. In the root spreading, HTS performs exploitation by modeling the root spreading mechanism of trees around themselves. The performance of the proposed algorithm is evaluated on multi-variable unconstraint numerical optimization benchmarks and constraint engineering problems. Comparing the proposed algorithm with a few other optimization algorithms shows the superiority of the HTS in terms of problem-solving success and finding the global optimum on most benchmark problems.

... • A new algorithm known as PPA [39] is proposed to search the optimal sizing and placement of DGs in the load flow analysis. Earlier, the PPA has been applied to certain problems in the power systems like optimal demand response programs [40]- [44], economic dispatch [45], automatic generation control [46] and optimal DGs' integration [47]. As per the authors' best of knowledge, the PPA has been an uncharted algorithm in the optimal sizing and placement problem of DGs in test networks in multiple rounds. ...

In recent years, the substantial upsurge of electricity demand has directly impacted the performance of the distribution networks concerning the active power losses and voltage drops. In such circumstances, the distributed generators (DGs) could uphold these concerns if they are optimally deployed in terms of sizing and placement. For this reason, in current research, the optimal deployment of DGs has been proposed with the plant propagation algorithm (PPA) to simultaneously maximize the total active power loss reduction and to upgrade the magnitude of the minimum bus voltage. Alongside, the authors have examined four rounds of DGs. In that context, the optimal deployment of one DG is investigated in the first round. In each succeeding round, the number of DGs is increased: in the second round, this investigation is carried out for two DGs, for three DGs in the third round, and finally, for four DGs in the fourth round of the investigation. The effectiveness of the proposed PPA has been tested on IEEE 33 and 69-bus test networks in the load flow analysis, and results are compared with the standard optimization algorithms. Thereafter, a post deployment economic assessment based on loss calculation has been undertaken out as well. The ANOVA test has also been performed for statistical evaluation of standard algorithms. The simulation results exhibit that the proposed algorithm outdo other algorithms both technically and economically. It has been seen that as the deployment of DGs is increased, the total active power losses and voltage drops are also reduced. In terms of economic assessments, the total cost decreases with the increased deployment of DGs in IEEE 33-bus test network, whereas, the total cost increases with the increased deployment of DGs in IEEE 69-bus test network.

... The continuous pursuit of researchers to develop a new algorithm is always a challenge and the undeterred endeavor has succeeded in evolving a new technique known as strawberry algorithm (SBA) [29]. This algorithm has been successfully applied to many engineering fields such as control, energy etc. [7,8,22,23]. The present author attempted to carry out exhaustive studies to determine the worthiness of SBA applied to antenna array synthesis i.e. to design the inter element spacings and their excitation amplitudes. ...

In this article attempt has been made for the first time to apply the reported strawberry optimization technique to antenna array synthesis problem. The algorithm is further modified by reinforcing it with adaptive values for the two key parameters known as runner length and root length embedded in the mathematical expression governing the movement of the mother plant from one position to another in the search space to locate the optimum solution. The case studies cited here refer to linear and circular array configurations. The design constraints are limited to minimizing the side lobe level and restricting the first null beam width, which play significant roles in antenna array performances. The important features which greatly influence in achieving the said objectives are either placement of antenna elements or amplitudes of excitations of these elements or both. And the recently reported nature inspired metaheuristic optimization algorithms have addressed to these antenna problems quite effectively and the application of the strawberry algorithm for the first time and the unraveling of a new algorithm known as reduced step size strawberry algorithm (rss-SBA) a variant of the existing SBA have shown quite exciting results thus opening avenues for these techniques to be the potential contenders in the race of authenticating their positions in the domain of nonlinear function optimization.

Demand-side management in the smart grid often consists of optimizing energy-related objective functions, with respect to variables, in the presence of constraints expressing electrical consumption habits. These functions are often related to the user’s electricity invoice (cost) or to the peak energy consumption (peak-to-average energy ratio), which can cause electrical network failure on a large scale. However, the growth in energy demand, especially in emerging countries, is causing a serious energy crisis. This is why several studies focus on these optimization approaches. To our knowledge, no article aims to collect and analyze the results of research on peak-to-average energy consumption ratio and cost optimization using a systematic reproducible method. Our goal is to fill this gap by presenting a systematic mapping study on the subject, spanning the last decade (2013–2022). The methodology used first consisted of searching digital libraries according to a specific search string (104 relevant studies out of 684). The next step relied on an analysis of the works (classified using 13 criteria) according to 5 research questions linked to algorithmic trends, energy source, building type, optimization objectives and pricing schemes. Some main results are the predominance of the genetic algorithms heuristics, an insufficient focus on renewable energy and storage systems, a bias in favor of residential buildings and a preference for real-time pricing schemes. The main conclusions are related to the promising hybridization between the genetic algorithms and swarm optimization approaches, as well as a greater integration of user preferences in the optimization. Moreover, there is a need for accurate renewable and storage models, as well as for broadening the optimization scope to other objectives such as CO2 emissions or communications load.

The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints.

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper’s main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.

Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer's flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

Optimizing the power demand for smart home appliances in a smart grid is the primary challenge faced by power supplier companies, particularly during peak periods, due to its considerable effect on the stability of a power system. Therefore, power supplier companies have introduced dynamic pricing schemes that provide different prices for a time horizon in which electricity prices are higher during peak periods due to the high power demand and lower during off-peak periods. The problem of scheduling smart home appliances at appropriate periods in a predefined time horizon in accordance with a dynamic pricing scheme is called power scheduling problem in a smart home (PSPSH). The primary objectives in addressing PSPSH are to reduce the electricity bill of users and maintain the stability of a power system by reducing the ratio of the highest power demand to the average power demand, known as the peak-to-average ratio, and to improve user comfort level by reducing the waiting time for appliances. In this paper, we review the most pertinent studies on optimization methods that address PSPSH. The reviewed studies are classified into exact algorithms and metaheuristic algorithms. The latter is categorized into single-based, population-based, and hybrid metaheuristic algorithms. Accordingly, a critical analysis of state-of-the-art methods are provided and possible future directions are also discussed.

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.

In this work, we evaluate performance of home energy management system(HEM) by using three meta-heuristic optimization techniques: Harmony search algorithm (HSA), Bacterial foraging optimization (BFO) and Enhanced deferential evolution(EDE). We categorize appliances into three groups on the basis of their energy consumption pattern. Real time pricing (RTP) scheme is used for electricity bill calculation. Our objectives are to minimize electricity cost, energy consumption, reduction in peak to average ratio while maximizing user comfort. However, there exists a trade-off between different objectives. Our simulation results show that there exist a trade-off between user comfort and cost. Results also show that in terms of cost HSA perform better among other techniques.

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.

As global energy problem is becoming more significant, energy companies worldwide are resorting Time of Use (ToU) tariff. The project aims at developing a smart Home Energy Management System (HEMS) that helps consumers to manage their loads according to the time of use tariff system and in turn help in bringing down the cost incurred. RFID (Radio Frequency Identification) is the method chosen to identify the devices plugged in at home and this information is displayed on a mobile application. When the rates of energy consumption are high during particular periods during the day, the smart HEMS notifies the user through the mobile application about the energy being consumed during higher tariff periods and the corresponding cost incurred.

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.

This paper proposes a new numerical optimization algorithm inspired by the
strawberry plant for solving complicated engineering problems. Plants like
strawberry develop both runners and roots for propagation and search for water
resources and minerals. In these plants, runners and roots can be thought of as
tools for global and local searches, respectively. The proposed algorithm has
three main differences with the trivial nature-inspired optimization
algorithms: duplication-elimination of the computational agents at all
iterations, subjecting all agents to both small and large movements from the
beginning to end, and the lack of communication (information exchange) between
agents. Moreover, it has the advantage of using only three parameters to be
tuned by user. This algorithm is applied to standard test functions and the
results are compared with GA and PSO. The proposed algorithm is also used to
solve an open problem in the field of robust control theory. These simulations
show that the proposed algorithm can very effectively solve complicated
optimization problems.

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.

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.

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.

The 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 and per unit cost. The main objective of this research article 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 for both single and multiple user scenarios.

In future smart grids, the electricity suppliers can modify the customers' load consumption pattern by implementing appropriate DSM (demand side management) programs using smart meters. Most of the existing studies on DSM, only consider one utility company in the supplier side. In this paper, the possibility of existing more than one supplier in the smart grid is addressed by modeling the DSM problem as two non-cooperative games: the supplier side game, and the customer side game. In the first game, the suppliers' profit maximization problem is formulated by applying supply function bidding mechanism. In the proposed mechanism, the electricity suppliers submit their bids to the DSM center, where the electricity price is computed and is sent to the customers. In the second game, the customers aim to determine optimal load profile to maximize their daily payoff. The existence and uniqueness of the Nash equilibrium in the mentioned games are explored and a computationally tractable distributed algorithm is designed to determine the equilibrium. Simulations are performed for a smart grid system with 3 suppliers and 1000 customers. Simulation results demonstrate the superior performance of the proposed mechanism in reducing the peak load and increasing the suppliers' profit and the customers' payoff.

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.

With the development of smart grid, residents have the opportunity to schedule their power usage in the home by themselves for the purpose of reducing electricity expense and alleviating the power peak-to-average ratio (PAR). In this paper, we first introduce a general architecture of energy management system (EMS) in a home area network (HAN) based on the smart grid and then propose an efficient scheduling method for home power usage. The home gateway (HG) receives the demand response (DR) information indicating the real-time electricity price that is transferred to an energy management controller (EMC). With the DR, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. Accordingly, all appliances in the home operate automatically in the most cost-effective way. When only the real-time pricing (RTP) model is adopted, there is the possibility that most appliances would operate during the time with the lowest electricity price, and this may damage the entire electricity system due to the high PAR. In our research, we combine RTP with the inclining block rate (IBR) model. By adopting this combined pricing model, our proposed power scheduling method would effectively reduce both the electricity cost and PAR, thereby, strengthening the stability of the entire electricity system. Because these kinds of optimization problems are usually nonlinear, we use a genetic algorithm to solve this problem.

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.

An integer linear programming based optimization for home demandside management in smart grid

- Ziming Zhu

Zhu, Ziming, et al. "An integer linear programming based optimization for home demandside management in smart grid." Innovative Smart Grid Technologies (ISGT), 2012 IEEE PES.
IEEE, 2012.

Advanced Information Networking and Applications Workshops (WAINA)

- Ayesha Zafar

Zafar, Ayesha, et al. "A meta-heuristic home energy management system." Advanced Information Networking and Applications Workshops (WAINA), 2017 31st International Conference
on. IEEE, 2017.