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An Approach Towards Efficient Energy Distribution and Power Flow Management in Smart Grid using Various Meta Heuristic Techniques
Abstract
Smart grid is an innovative and novel technology successfully implemented by the use of different communication methods. Demand side management (DSM) plays a signi�cant role in the management of load and energy consumption in order to reduce cost in the smart grids. Smart buildings and smart homes are usually considered important for reducing the electricity consumption by home energy management controllers (HEMC). Within the research community, different optimization techniques have been designed for home energy management system (HEMS). In this work, the performance of few heuristic algorithms, i.e., genetic algorithm (GA), harmony search algorithm (HSA), enhanced differential evolution (EDE), tabu search (TS) and backtracking search optimization algorithm (BSOA) is evaluated for optimization in residential area. Also, an optimal power
flow (OPF) problem is formulated for economic operation of electrical system while
incorporating stochastic and intermittent nature of solar photovoltaic (PV) and wind generators. Proposed techniques are used for efficient scheduling of smart appliances in smart homes on the customer side as well as for the optimal setting of control variables on the supply side. Besides, minimization of power generation cost, concern on environment is also taken into account and reduction of carbon emission factor is included into the objective function. Simulations were performed in MATLAB by using real time pricing (RTP) tariff and IEEE standard bus test systems. Evaluated results proved that our de�ned goals of cost reduction, improvement of user comfort (UC) level and minimization of peak to average ratio (PAR) are achieved.
... The penalty cost coefficient helps to determine the debit that is produced from the overestimation or underestimation of solar irradiance. The probability distribution function for the power output of variable solar irradiance can also be determined using the Weibull probability distribution function [21]- [22]- [23]. ...
This paper presents a selective survey of papers, books, and reports that articulate recent trends of Security Constrained Economic Dispatch (SCED) of integrated renewable energy systems (IRES). The time-period under consideration is 2008 through 2020. This is done to provide an up-to-date review of the recent, major advancements in the SCED, and state-of-the-art since 2008. This helps identify further challenges needed in adopting smarter grids, and indicate ways to address these challenges. The study was conducted in three areas of interest that are relevant for articulating the recent trends of SCED. These areas are (i) SCED of power systems with IRES, (ii) SCED mathematical formulation and solution methods, and (iii) SCED challenges. The review results and research directions deduce that the state of the art research is not enough and needs special attention on following the path of artificial intelligence-based optimization.
This paper presents a model for optimal energy management under the time-of-use (ToU) and critical peak price (CPP) market in a microgrid. The microgrid consists of intermittent dispatchable distributed generators, energy storage systems, and multi-home load demands. The optimal energy management problem is a challenging task due to the inherent stochastic behavior of the renewable energy resources. In the past, medium-sized distributed energy resource generation was injected into the main grid with no feasible control mechanism to prevent the waste of power generated by a distributed energy resource which has no control mechanism, especially when the grid power limit is altered. Thus, a Jaya-based optimization method is proposed to shift dispatchable distributed generators within the ToU and CPP scheduling horizon. The proposed model coordinates the power supply of the microgrid components, and trades with the main grid to reduce its fuel costs, production costs, and also maximize the monetary profit from sales revenue. The proposed method is implemented on two microgrid operations: the standalone and grid-connected modes. The simulation results are compared with other optimization methods: enhanced differential evolution (EDE) and strawberry algorithm (SBA). Finally, simulation results show that the Jaya-based optimization method minimizes the fuel cost by up to 38.13%, production cost by up to 93.89%, and yields a monetary benefit of up to 72.78% from sales revenue.
In this paper, we propose a demand side management (DSM) scheme in the residential area for electricity cost and peak to average ratio (PAR) alleviation with maximum users’ satisfaction. For this purpose, we implement state-of-the-art algorithms: enhanced differential evolution (EDE)and teacher learning-based optimization (TLBO). Furthermore, we propose a hybrid technique (HT) having the best features of both aforementioned algorithms. We consider a system model for single smart home as well as for a community (multiple homes) and each home consists of multiple appliances with different priorities. The priority is assigned (to each appliance) by electricity consumers and then the proposed scheme finds an optimal solution according to the assigned priorities. Day-ahead real time pricing (DA-RTP) and critical peak pricing (CPP) are used for electricity cost calculation. To validate our proposed scheme, simulations are carried out and results show that our proposed scheme efficiently achieves the aforementioned objectives. However, when we perform a comparison with existing schemes, HT outperforms other state-of-the-art schemes(TLBO and EDE) in terms of electricity cost and PAR reduction while minimizing the average waiting time.
Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio(PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid ofWDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.
Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.
In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.
Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM techniques ignore priority defined by consumers. In this paper, we present priority-induced DSM strategy based on the load shifting tech-nique considering various energy cycles of an appliance. The day-ahead load shifting technique proposed is mathematically formulated and mapped to multiple knapsack problem to mitigate the rebound peaks. The autonomous energy management controller proposed embeds three meta-heuristic optimization techniques; genetic algorithm, enhanced differential evolu-tion, and binary particle swarm optimization along with optimal stopping rule, which is used for solving the load shifting problem. Simulations are carried out using three different appliances and the results validate that the proposed DSM strategy successfully shifts the appliance operations to off-peak time slots, which consequently leads to substantial electricity cost savings in reasonable waiting time, and also helps in reducing the peak load demand from the smart grid. In addition, we calculate the feasible regions to show the relationship between cost, energy consumption, and delay.
A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. Available from: https://www.researchgate.net/publication/323945280_A_priority-induced_demand_side_management_system_to_mitigate_rebound_peaks_using_multiple_knapsack [accessed Mar 26 2018].
Internet of Things (IoT) based Smart grids (SGs) represent a vision of future power systems which helps to provide electricity in a smart and user friendly way. Demand side management is one of the most important component of a SG which allows energy consumers to change their electricity consumption patterns to reduce the electricity consumption cost. In this work, we propose a home energy management system which helps to achieve our desired objectives: reduced electricity consumption cost, peak to average ratio (PAR) and maximize user comfort. For this purpose, we have proposed a scheduling technique which is a hybrid of already existing optimization techniques: bacteria foraging algorithm (BFA) and harmony search algorithm (HSA) and is named as hybrid bacterial harmony (HBH) algorithm. Being producer of electricity units to the consumers, a utility establishes an incentive based pricing tariff; we, on top of it have employed seasonal time of use (ToU) tariff which allows consumers to take decisions regarding their consumption patterns. Moreover, we introduce the concept of coordination among smart appliances using dynamic programming (DP) approach. The coordination among appliances is achieved by the help of the large data generated from the appliances of multiple homes with the joint work of heuristic techniques and DP. The resultant coordination not only reduces the electricity cost but also increases the user comfort. At last, we evaluate the performance of our proposed energy management system using our proposed optimization technique HBH. To comparatively evaluate the performance of our proposed technique, we compare it with already existing techniques. Simulation results validate that the proposed technique effectively accomplish the desired objectives while considering the consumer comfort.
Optimal Power Flow (OPF) using the Multi
objective functions is becoming one of the most important issues
in power system. This paper presents a new method to solve the
highly constrained multi-objective optimization. In the proposed
approach, Multi Objective Backtracking Search Algorithm
(MOBSA) is used as the main optimizer for optimal adjustments
of the power system control variables of the OPF problem.
Considering several objectives such as fuel cost minimization,
voltage improvement, and voltage stability enhancement. The
proposed technique has been examined and tested for standard
IEEE 30 bus system. The MOBSA method is demonstrated and
compared with several intelligence heuristic algorithms.
The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA)to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort
IIn this paper, we propose a home energy management system which employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique which helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our realtime rescheduling problem as knapsack problem and solve it through dynamic programming. This study also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.
As an important part of demand-side management, residential demand response (DR) can not only reduce consumer’s electricity costs, but also improve the stability of power system operation. In this regard, this paper proposes an optimal scheduling model of household appliances for smart home energy management considering DR. The model includes electricity cost, incentive and inconvenience of consumers under time-of-use (TOU) electricity price. Further, this paper discusses the influence of inconvenience weighting factor on total costs. At the same time, the influence of incentive on optimization results is also analyzed. The simulation results show the effectiveness of the proposed model, which can reduce 34.71% of consumer’s total costs. It also illustrates that the total costs will be raised with the increase in inconvenience weighting factor. Thus, consumers will choose whether to participate in DR programs according to their preferences. Moreover, the result demonstrates that incentives are conducive to shifting load and reducing the consumer’s total energy costs. The presented study provides new insight for the applications of residential DR.
Demand Side Management (DSM) will play a significant role in the future smart grid by managing loads in a smart way. DSM programs, realized via Home Energy Management (HEM) systems for smart cities, provide many benefits; consumers enjoy electricity price savings and utility operates at reduced peak demand. In this paper, Evolutionary Algorithms (EAs) (Binary Particle Swarm Optimization (BPSO), Genetic Algorithm (GA) and Cuckoo search) based DSM model for scheduling the appliances of residential users is presented. The model is simulated in Time of Use (ToU) pricing environment for three cases: (i) traditional homes, (ii) smart homes, and (iii) smart homes with Renewable Energy Sources (RES). Simulation results show that the proposed model optimally schedules the appliances resulting in electricity bill and peaks reductions.
In 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.
This paper proposes Improved Colliding Bodies Optimization (ICBO) algorithm to solve
efficiently the Optimal Power Flow (OPF) problem. Several objectives, constraints and
formulations at normal and preventive operating conditions are used to model the OPF problem.
Applications are carried out on three IEEE standard test systems through 16 case studies to assess
the efficiency and the robustness of the developed ICBO algorithm. A proposed performance
evaluation procedure is proposed to measure the strength and robustness of the proposed ICBO
against numerous optimization algorithms. Moreover, a new comparison approach is developed to
compare the ICBO with the standard CBO and other well-known algorithms. The obtained results
demonstrate the potential of the developed algorithm to solve efficiently different OPF problems
compared to the reported optimization algorithms in the literature.
In the operation of a smart grid (SG), day-ahead load forecasting (DLF) is an important task. The SG can enhance the management of its conventional and renewable resources with a more accurate DLF model. However, DLF model development is highly challenging due to the non-linear characteristics of load time series in SGs. In the literature, DLF models do exist; however, these models trade off between execution time and forecast accuracy. The newly-proposed DLF model will be able to accurately predict the load of the next day with a fair enough execution time. Our proposed model consists of three modules; the data preparation module, feature selection and the forecast module. The first module makes the historical load curve compatible with the feature selection module. The second module removes redundant and irrelevant features from the input data. The third module, which consists of an artificial neural network (ANN), predicts future load on the basis of selected features. Moreover, the forecast module uses a sigmoid function for activation and a multi-variate auto-regressive model for weight updating during the training process. Simulations are conducted in MATLAB to validate the performance of our newly-proposed DLF model in terms of accuracy and execution time. Results show that our proposed modified feature selection and modified ANN (m(FS + ANN))-based model for SGs is able to capture the non-linearity(ies) in the history load curve with 97.11% accuracy. Moreover, this accuracy is achieved at the cost of a fair enough execution time, i.e., we have decreased the average execution time of the existing FS + ANN-based model by 38.50%.
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
Recently, krill herd algorithm (KHA) is proposed for the solution of global optimization problem. Various chaotic maps are considered in the proposed chaotic KHA (CKHA) of the present work to improve the performance of basic KHA method. It is observed that Logistic map based CKHA offers better results as compared other chaotic maps. The performance of the proposed CKHA is tested on standard 26-bus and IEEE 57-bus test power systems for the solution of optimal power flow of power system with different objectives that reflect minimization of fuel cost or active power loss or sum of total voltage deviation. Results obtained by using CKHA are compared to other evolutionary optimization techniques surfaced in the recent state-of-the-art literature. The results presented in this paper show that the proposed CKHA algorithm outperforms the other techniques in terms of convergence rate and global search ability.
In this paper, an energy consumption management is considered for households (users) in a residential smart grid network. In each house, there are two types of demands, essential and flexible demands, where the flexible demands are further categorized into delay-sensitive and delay-tolerant demands. The delay-sensitive demands have higher priority to be served than the delay-tolerant demands. Meanwhile, in order to decrease the delay of delay-tolerant demands, such demands are allowed to be upgraded to the high-priority queue (i.e., the same queue that serves the delay-sensitive demands) with a given probability. An optimization problem is then formulated to minimize the total electricity cost and the operation delay of flexible demands by obtaining the optimal energy management decisions. Based on adaptive dynamic programming, a centralized algorithm is proposed to solve the optimization problem. In addition, a distributed algorithm is designed for practical implementation and the neural network is employed to estimate the pricing or demands when such system information is not known. Simulation results show that the proposed schemes can provide effective management for household electricity usage and reduce the operation delay for the flexible demands.
Distributed Generation (DG) sources based on Renewable Energy (RE) can be the fastest growing power resources in distribution systems due to their environmental friendliness and also the limited sources of fossil fuels. In general, the optimal location and size of DG units have profoundly impacted on the system losses in a distribution network. In the present article, the Particle Swarm Optimization (PSO) algorithm is employed to find the optimal location and size of DG units in a distribution system. The optimal location and size of DG units are determined on the basis of a multi-objective strategy as follows: (i) the minimization of network power losses, (ii) the minimization of the total costs of Distributed Energy Resources (DERs), (iii) the improvement of voltage stability, and (iv) the minimization of greenhouse gas emissions. The related distribution system was assumed to be composed of the fuel cells, wind turbines, photovoltaic arrays, and battery storages. The electrical, cooling, and heating loads were also considered in this article. The heating and cooling requirements of the system consist of time varying water heating load, space heating load, and space cooling load. In this study, the waste and fuel cell were used to produce the required heating and cooling loads in the distribution system. In addition, the absorption chiller was used to supply the required space cooling loads. A detailed performance analysis was carried out on 13 bus radial distribution system to demonstrate the effectiveness of the proposed methodology.
Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims {to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR)}. In this paper, we design a HEM controller on the {basis} of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed {a} hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately $34\%$ PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction {, as} it reduces approximately $36\%$ cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.
A new optimal power flow model for wind, solar, and solar-thermal bundled power scheduling and dispatch is proposed, incorporating the deviation incentive/penalty charges for renewable energy introduced in India. The multiobjective function is solved using the flower pollination algorithm; the scheme is successfully tested on the IEEE 30-bus and Indian utility 30-bus systems. The forecasting error constraints introduced in renewable energy scheduling and dispatch are demonstrated to be beneficial in several aspects. Solar-thermal bundling is shown to create win-win situations for thermal and solar generators. The effectiveness of the flower pollination algorithm in solving optimal power flow models is proved.
This work presents a novel Moth Swarm Algorithm (MSA), inspired by the orientation of moths towards moonlight to solve constrained Optimal Power Flow (OPF) problem. The associative learning mechanism with immediate memory and population diversity crossover for Lévy-mutation have been proposed to improve exploitation and exploration ability, respectively, in addition to adaptive Gaussian walks and spiral motion. The MSA and four heuristic search algorithms are carried out on the IEEE 30-bus, 57-bus and IEEE 118-bus power systems. These approaches are applied to optimize the control variables such as real power generations, load tap changer ratios, bus voltages and shunt capacitance values under several power system constraints. Fourteen different cases are executed on different curves of fuel cost (e.g., quadratic, valve-loading effects, multi-fuels options), environmental pollution emission, active power loss, voltage profile and voltage stability for contingency and normal conditions, in single and multi objective optimization space. Furthermore, the impacts of the updating mechanism of optimizers on those objective functions are investigated. The effectiveness and superiority of the MSA have been demonstrated in comparison with many recently published OPF solution
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 article proposes a reliable computational paradigm for solving the optimal voltage regulation problem in power distribution systems in the presence of distributed generators. The main idea is to integrate elements of multi-objective optimization theory and decision analysis for computing a reliable problem solution, considering all the system uncertainties, and describing the multi-criteria aspects of the regulation problem. Detailed numerical simulations obtained from both balanced and unbalanced power systems are presented and discussed to demonstrate the effectiveness of the proposed methodology in the task of solving the optimal voltage regulation problem under different operating scenarios.
The drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly-proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.
In this paper, a simple and efficient nature inspired search method based on differential search algorithm (DSA) has been presented and used for optimal power flow (OPF) problem in power systems. By using the proposed DSA method, the power system parameters such as real power generations, bus voltages, load tap changer ratios and shunt capacitance values are optimized for the certain objective functions. Different types of single-objective and multi-objective functions on IEEE 9-bus, IEEE 30-bus and IEEE 57-bus power systems are used to test and verify the efficiency of the proposed DSA method. By comparing with several optimization methods, the results obtained by using the proposed DSA method are presented in detail. The results achieved in this work illustrate that the DSA method can successfully be used to solve the non-linear and non-convex problems related to power systems.
Microgrids are promising in reducing energy consumption and carbon emissions, compared with the current centralised energy generation systems. Smart homes are becoming popular for their lower energy cost and provision of comfort. Flexible energy-consuming household tasks can be scheduled co-ordinately among multiple smart homes to reduce economic cost and CO2. However, the electricity tariff is not always positively correlated with CO2 intensity. In this work, a mixed integer linear programming (MILP) model is proposed to schedule the energy consumption within smart homes using a microgrid system. The daily power consumption tasks are scheduled by coupling environmental and economic sustainability in a multi-objective optimisation with ε-constraint method. The two conflicting objectives are to minimise the daily energy cost and CO2 emissions. Distributed energy resources (DER) operation and electricity-consumption household tasks are scheduled based on electricity tariff, CO2 intensity and electricity task time window. The proposed model is implemented on a smart building of 30 homes under three different price schemes. Electricity tariff and CO2 intensity profiles of the UK are employed for the case study. The Pareto curves for cost and CO2 emissions present the trade-off between the two conflicting objectives.
This paper studies the power scheduling problem for residential consumers in smart grid. In general, the consumers have two types of electric appliances. The first type of appliances have flexible starting time and work continuously with a fixed power. The second type of appliances work with a flexible power in a predefined working time. The consumers can adjust the starting time of the first type of appliances or reduce the power consumption of the second type of appliances to reduce the payments. However, this will also incur discomfort to the consumers. Assuming the electricity price is announced by the service provider ahead of time, we propose a power scheduling strategy for the residential consumers to achieve a desired trade-off between the payments and the discomfort. The power scheduling is formulated as an optimization problem including integer and continuous variables. An optimal scheduling strategy is obtained by solving the optimization problem. Simulation results demonstrate that the scheduling strategy can achieve a desired tradeoff between the payments and the discomfort.
Demand Side Management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits.
This paper proposes a distributed Privacy-Friendly DSM system that preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of Additive White Gaussian Noise (AWGN).
We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern.
The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented.
This paper presents an adaptive group search optimization (AGSO) algorithm for solving optimal power flow (OPF) problem. In this study, different aspects of the OPF problem are considered to form the accurate multi-objective model. The system total operation cost, the total emission, and N-1 security index are first, second, and third ordered objectives, respectively. Additionally, to consider accurate model of the problem, transmission losses and different equality and inequality constrains, such as feasible operating ranges of generators (FOR) and power flow equations are taken into account. Moreover, this study presents adaptive form of conventional GSO to precise the convergence characteristic of GSO. The effectiveness and accuracy of the proposed method for solving the nonlinear and nonconvex problems is validated by carrying out simulation studies on sample benchmark test cases and 30-bus and 57-bus IEEE standard test systems. Based on the comprehensive simulation studies, the accuracy of the proposed method is validated.
Severe peak rebounds are likely in absence of a system-wide coordination among customers participating in demand response programs. This paper aims to establish a decentralized system-wide framework to coordinate demand response of residential customers in a smart grid. The objective of the framework is to modify system load profile provided that customers' payments are minimized, and their comfort and privacy are preserved. Home load management (HLM) modules, embedded in customers' smart meters are autonomous agents of the framework. The energy service provider iteratively exchanges load information with HLM modules in the hope of achieving his desired load profile. In each iteration, the service provider announces system load profile to HLM modules. The modules, keeping in mind their own financial and comfort constraints, nonsequentially send back load reschedule proposals to modify system load profile. The received proposals are judged whether they improve system load profile or not. HLM modules with accepted proposals apply their proposed schedules. The modified system load profile is then released, and HLM modules' new proposals are gathered and judged. This procedure is repeated to the point at which no further improvement in the system load profile can be experienced. Performance of the framework is shown by applying it to a system with 50 customers.
In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.
Demand Side Management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost as well as the per unit cost. The main objective of this paper is to develop a Generic Demand Side Management (G-DSM) model for residential users to reduce Peak-to-Average Ratio (PAR), total energy cost and Waiting Time of Appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G-DSM model is based on Genetic Algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G-DSM model both for single and multiple users scenarios.
Due to the increase rapidly of electricity demand and the deregulation of electricity markets, the energy networks are usually run close to their maximum capacity to transmit the needed power. Furthermore, the operators have to run the system to ensure its security and transient stability constraints under credible contingencies. Security and transient stability constrained optimal power flow (STSCOPF) problem can be illustrated as an extended OPF problem with additional line loading and rotor angle inequality constraints. This paper presents a new approach for STSCOPF solution by a chaotic artificial bee colony (CABC) algorithm based on chaos theory. The proposed algorithm is tested on IEEE 30-bus test system and New England 39-bus test system. The obtained results are compared to those obtained from previous studies in literature and the comparative results are given to show validity and effectiveness of proposed method.
The smart grid concept continues to evolve and various methods have been developed in order to enhance the energy efficiency of the electricity infrastructure. Demand Response (DR) is considered as the most cost-effective and reliable solution for the smoothing of the demand curve, when the system is under stress. DR refers to a procedure that is applied to motivate changes in the customers’ power consumption habits, in response to incentives regarding the electricity prices. In this paper, we provide a comprehensive review of various DR schemes and programs, based on the motivations offered to the consumers in order to participate in the program. We classify the proposed DR schemes according to their control mechanism, to the motivations offered to reduce the power consumption and to the DR decision variable. We also present various optimization models for the optimal control of the DR strategies that have been proposed so far. These models are also categorized, based on the target of the optimization procedure. The key aspects that should be considered in the optimization problem are the system’s constraints and the computational complexity of the applied optimization algorithm.
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.
This paper introduces the Backtracking Search Optimization Algorithm (BSA), a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. EAs are popular stochastic search algorithms that are widely used to solve non-linear, non-differentiable and complex numerical optimization problems. Current research aims at mitigating the effects of problems that are frequently encountered in EAs, such as excessive sensitivity to control parameters, premature convergence and slow computation. In this vein, development of BSA was motivated by studies that attempt to develop simpler and more effective search algorithms. Unlike many search algorithms, BSA has a single control parameter. Moreover, BSA’s problem-solving performance is not over sensitive to the initial value of this parameter. BSA has a simple structure that is effective, fast and capable of solving multimodal problems and that enables it to easily adapt to different numerical optimization problems. BSA’s strategy for generating a trial population includes two new crossover and mutation operators. BSA’s strategies for generating trial populations and controlling the amplitude of the search-direction matrix and search-space boundaries give it very powerful exploration and exploitation capabilities. In particular, BSA possesses a memory in which it stores a population from a randomly chosen previous generation for use in generating the search-direction matrix. Thus, BSA’s memory allows it to take advantage of experiences gained from previous generations when it generates a trial preparation. This paper uses the Wilcoxon Signed-Rank Test to statistically compare BSA’s effectiveness in solving numerical optimization problems with the performances of six widely used EA algorithms: PSO, CMAES, ABC, JDE, CLPSO and SADE. The comparison, which uses 75 boundary-constrained benchmark problems and three constrained real-world benchmark problems, shows that in general, BSA can solve the benchmark problems more successfully than the comparison algorithms.
We propose a consumption scheduling mechanism for home area load management in smart grid using integer linear programming (ILP) technique. The aim of the proposed scheduling is to minimise the peak hourly load in order to achieve an optimal (balanced) daily load schedule. The proposed mechanism is able to schedule both the optimal power and the optimal operation time for power-shiftable appliances and time-shiftable appliances respectively according to the power consumption patterns of all the individual appliances. Simulation results based on home and neighbourhood area scenarios have been presented to demonstrate the effectiveness of the proposed technique.
Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.