Conference Paper

Game Theory based Electric Price Tariff and Salp Swarm Algorithm for Demand Side Management

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Abstract

The emergence of Demand Response(DR) program optimizes the energy consumption pattern of customers and improves the efficacy of energy supply. The pricing infra structure of DR program is time-based rate where prices changed according to user usage. The variation in price rate is due to the consume electricity and extra generation cost. The main objective of DR is to encourage the consumer to shifts the peak load and get incentives in terms of cost reduction. Users who shift the load and who did not have to pay the same rate. In this work, game theory based pricing strategy is evaluated where each user have different price rates according to the consume energy during Shoulder-peak and On-peak hours. Moreover, to avoid peek formation during the Off-peak hours Salp swarm algorithm is used to schedule the home appliances. The experimental results prove the effectiveness of proposed pricing scheme as well proposed scheduling scheme.

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... The SSA algorithm has been applied to various optimization problems because of its simple mechanism, dynamic nature and strong global search ability. Such as extracting the parameters of photovoltaic system cell [63,64], optimization of softwaredefined networks [65], train neural networks [66,67], optimize parameters of soil water retention [68], tariff optimization in electrical systems [69], image segmentation [70,71], target localization [72], optimal power flow problem [73], estimation of optimal parameters of polymer exchange membrane fuel cells [74], feature selection [75,76,77,78,79,80] and others. Although the SSA algorithm is very competitive, it still suffers from some limitations such as poor convergence, unbalanced exploration and exploitation capacities, which may lead to local optimum stagnation when solving some intractable optimization problems. ...
Article
Full-text available
Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmeta-heuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.
... The graphical results show a significant reduction in electricity costs for multiple scenarios. In [131], SSA was proposed for household appliances management with game theory based pricing strategy, in which every user has different tariff. ...
Thesis
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Due to the rapidly developing of electric power system across the world in response to technical, economic and environmental developments, modern power systems often operate proximate to their maximal limits, engendering voltage instability risks in electric grid. On the other hand, excessive penetration of renewable energy sources into electrical grids may lead to many problems and operational limit violations, such as over and under voltages, active power losses and overloading of transmission lines, power plants failure, voltage instability risks and users discomfort. These problems happen when the system exceeds maximal operational capability (MOC) limit. In this thesis, firstly, various meta-heuristic optimization techniques have been developed and implemented to deal with different power system problems, such as single and multi-objective optimal reactive power dispatch problem. Since the characteristics of optimal reactive power dispatch (ORPD) in nature non-linear and non-convex and are consisting of mix of discrete and continuous variables; some non-conventional optimization techniques are developed and adopted to deal with discrete ORPD problem in large-scale electric grids. Technology advancement for green energy and its integration to the electric power system (EPS) has gathered substantial interest in the last couple of decades. Incorporating such resources has proven to reduce power losses and improve the reliability of electrical network. However, hyper-production or hypo-production of these resources in electric grid has imposed additional operational and control issues in voltage-regulation, system stability, and feasibility of solutions. Renewables incorporation into electric grid has led to significant changes in the types of consumption as well as dramatic and direct changes in the needs of optimal planning and operation of electric grids. To assess the suitability of the nonclassical optimization techniques for modern power systems, stochastic optimal power flow (OPF) problem with uncertain reserves from renewable energy sources was studied under different scenarios, including valve pointe effect and gas emission. Simulation results demonstrated that with hybrid generation system we could benefit with 2.4 % per hour cost reduction compared to traditional grid that based only on thermal generations as power sources. In addition, a new application of slim mould algorithm for practical optimal power flow with integration of renewables was conducted on Algerian electricity grid (DZA114-220/60 kV). Different cases were studied, where the feasibility of solutions and all control variables are deeply discussed. Hence, hybrid generation system is more effective and viable than classical system. In smart grid, efficient load management can help balance, reduce the burdensome on the electric network, and minimize operational electricity-cost. Robust optimization is a method that is used increasingly in the scheduling of household loads through demand side control. To this end, demand side management (DSM) scheme based on Meta-heuristic optimization techniques is proposed for home energy management system. The main objective behind this study is to adjust the peak demand and offering the total energy required at minimum cost with high quality. More precisely, in order to make consumers aware of their effective and essential contributions to helping the operator system during emergency cases (requests during peak hours). Simulation results showed that the proposed DSM scheme based meta-heuristic algorithms enrolling higher reduction in the total energy cost up to 26% compared to the base case and peak average ratio is curtailed by 55 %. In overall, the obtained results demonstrate significant improvement in energy quality, electric power system security, and notable reduction of peak demand in offering total energy required at minimum cost.
... The graphical results show a significant reduction in electricity costs for multiple scenarios. In Khalid et al. (2019), SSA was proposed for household appliances management with game theory based pricing strategy, in which every user have different tariff. The authors discussed how to distribute the extra generation charges onto each consumer basing on the electricity load profile. ...
Article
Full-text available
With arrival of advanced technologies, automated appliances in residential sector are still in unlimited growth. Therefore, the design of new management schemes becomes necessary to be achieved for the electricity demand in an effort to ensure safety of domestic installations. To this end, the Demand Side Management (DSM) is one of suggested solution which played a significant role in micro-grid and Smart Grid (SG) systems. DSM program allows end-users to communicate with the grid operator so they can contribute in making decisions and assist the utilities to reduce the peak power demand through peak periods. This can be done by managing loads in a smart way, while keeping up customer loyalty. Nowadays, several DSM programs are proposed in the literature, almost all of them are focused on the domestic sector energy management system (EMS). In this original work, four heuristics optimization algorithms are proposed for energy scheduling in smart home, which are: bat algorithm (BA), grey wolf optimizer (GWO), moth flam optimization (MFO), algorithm, and Harris hawks optimization (HHO) algorithm. The proposed model used in this experiment is based on two different electricity pricing schemes: Critical-Peak-Price (CPP) and Real-Time-Price (RTP). In addition, two operational time intervals (OTI) (60 min and 12 min) were considered to evaluate the consumer's demand and behavior of the suggested scheme. Simulation results show that the suggested model schedules the appliances in an optimal way, resulting in electricity-cost and peaks reductions without compromising users' comfort. Hence, results confirm the superiority of HHO algorithm in comparison with other optimization techniques.
... Moreover, to avoid the peak formation during the Off-peak hours, meta-heuristic based scheduling algorithm is presented in this work (see Sections 1.10.6 and 3.7). The initial version of the proposed solution is published in [117], which is further extended and published in [118]. ...
Thesis
Full-text available
Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the consumer's nature of demand. In return, utilities pay incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand and irregular electricity load profile in residential area have encouraged us to propose an efficient home energy management system for optimal scheduling of home appliances. Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. However, these optimization techniques behave stochastically according to the situation. For this reason, we have proposed different optimization techniques for different scenarios. The objectives of this thesis include: reduction in electricity bill and peak to average ratio, minimization of waiting time to start appliances (comfort maximization) and minimization of wastage of surplus energy by exploiting the coordination among appliances and homes. In order to meet the electricity demand of the consumers, the energy consumption patterns of a consumer are maintained through scheduling the appliances in day-ahead and realtime bases. It is applicable by the defined fitness criterion for the proposed hybrid bacterial foraging genetic algorithm and hybrid elephant adaptive cuckoo search optimization techniques, which helps in balancing the load during On-peak and Off-peak hours. Moreover, the concept of coordination and coalition among home appliances is presented for real-time scheduling. The fitness criterion helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of the appliance. A multi-objective optimization based solution is proposed to resolve the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. Two optimization techniques: binary multiobjective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms are proposed to obtain the Pareto front. The main objective of DR is to encourage the consumer to shift the peak load and gets incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this thesis, Game Theory (GT) based Time of Use pricing model is presented to define the pricing strategy for On-peak and Off-peak hours. The price is defined for each user according to the utilized load using coalitional GT. Further, the proposed pricing model is analyzed for scheduled and unscheduled load. In this regards, Salp swarm and rainfall algorithms are used for scheduling of appliances and an aggregated fitness criterion is defined for load shifting to avoid the peak rebound effect. We also proposed the coordination and coalition based Energy Management System-as-a- Service on Fog (EMSaaS_Fog). With the increase in number of electricity consumers, the computational complexity of energy management system is becoming a threat for efficiency of a system in real-time environment. To deal with this dilemma, the utility shifts computational and storage units on cloud and fog. The proposed EMSaaS_Fog effectively handles the coalition among the apartments within a building to maintain balance between the demand and supply. Moreover, we consider a small community, which consists of multiple smart homes. Microgrid is installed at each residence for electricity generation. It is connected with the fog server to share and store information. Smart energy consumers are able to share detail of excess energy with each other through the fog server.
... The electricity demanded by participants can be represented as D total . Real-time price, Time of Use (ToU) and curtailable/interruptible pricing tariffs are considered as non-linear dynamic pricing [40,41]. For the residential sector in this paper modified ToU tariff is used to calculate the dynamic price of electricity. ...
Article
With the advent of advancements in the power sector, various new methods have been devised to meet modern society's electricity needs. To cope with these large sets of electronic device's current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are twofold ; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly , at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.
... The SSA algorithm because of its simple structure and dynamic nature has been applied to a large set of optimization problems. The major applications being optimized are neural networks [11] , parameters of soil water retention [12] , feature selection [13] , tariff optimization in electrical systems [14] and others. Apart from the application part, work has also been done to improve the working capabilities of SSA. ...
Article
Salp swarm algorithm is a recent introduction in the field of swarm intelligent algorithms and has proved its worth over various research domains. Though it is a competitive algorithm but it has been found that salp swarm algorithm suffers from various problems including poor exploitation, slow convergence and unbalanced exploration and exploitation operation. In present work, four major modifications have been added to salp swarm algorithm in order to make it self-adaptive and the proposed algorithm has been named as adaptive salp swarm algorithm. The modifications include division of generations and logarithmic adaptive parameters to control the extent of exploration and exploitation, enhanced exploitation phase to improve the local search and linearly decreasing population adaptation to reduce the total number of function evaluations. The performance of the proposed algorithm is tested on benchmark problems and further applied for optimization of transmission parameters in cognitive radio system. From the experimental results, it has been found that the proposed adaptive salp swarm algorithm is highly competitive and provides better results when compared with bat algorithm, grey wolf optimization, teacher learning based algorithm, dragonfly algorithm and others. Convergence profiles and statistical tests further validate the results.
... Game Theory (GT), which is a principle for life form and informatics of intelligent decision-making, applies to a wide range of behavioral relationships [40]. The coalition-based GT model has been used in [41] to formalize the cost scheme model. Usually, electricity is charged per unit using Demand Response (DR); dynamic or time-based price models. ...
Chapter
Swarm Intelligence (SI) is referred to the social conduct emerging within decentralized and self-organization of swarms. These swarms are summarized as the well-known examples such as bird groups, fish schools, and the most social in insects species for instance bees, termites, and ants. Among those, Salp Swarm Algorithm (SSA), that has been successfully utilized and held in different fields of optimization, engineering practice, and real-world problems, so far. This review carries out a extensive study for the present status of publications, advances, applications, variants with SSA including its modifications, population topology, hybridization, extensions, theoretical analysis, and parallel implementation in order to show its potential to show its potential to overcome many practical optimization issues. Further, this review will be greatly useful for the researchers and algorithm developers analyzing at Swarm Intelligence, especially SSA to use this simple and yet very efficient approach for several tough optimization issues.
... The main aim of demand response is to help the user to groups the peak load and receive purposes in terms of cost reduction: users who change the load and who did not have to give the same price. In this research [96], a game theory-based pricing approach is assessed where each user has various price rates based on the use of energy during shoulder-peak and on-peak hours. Furthermore, to avert peek formation through the off-peak hours SSA is utilized to schedule the various home appliances. ...
Article
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This paper completely introduces an exhaustive and a comprehensive review of the so-called salp swarm algorithm (SSA) and discussions its main characteristics. SSA is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields, such as machine learning, engineering design, wireless networking, image processing, and power energy. This review shows the available literature on SSA, including its variants, like binary, modifications and multi-objective. Followed by its applications, assessment and evaluation, and finally the conclusions, which focus on the current works on SSA, suggest possible future research directions.
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p>New Time of Use (ToU) tariff scheme known as Enhanced ToU (EToU) has been introduced on 1st January 2016 for industrial customers in Malaysia. EToU scheme is the advanced version of current ToU where the daily time frame is divided into six period blocks, as compared to only two in the existing ToU. Mid-peak tariff is introduced on top of peak-hour and off-peak tariff. The new scheme is designed to reduce Malaysia’s peak hour electricity demand. On customer side, they could be benefited from the low off-peak tariff by simply shifting their consumption. However, it depends on their consumption profile and their flexibility in shifting their consumption. Since EToU scheme is voluntary, each customer needs to perform cost-benefit analysis before deciding to switch into the scheme. This paper analyzes this problem by considering EToU tariff scheme for industry and customer’s electricity consumption profile. Case studies using different practical data from different industries are presented and discussed in this paper.</p
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With the increasing demand for electricity and the advent of smart grids, developed countries are establishing demand side management (DSM) techniques to influence consumption patterns. The use of dynamic pricing strategies has emerged as a powerful DSM tool to optimize the energy consumption pattern of consumers and simultaneously improve the overall efficacy of the energy market. The main objective of the dynamic pricing strategy is to encourage consumers to participate in peak load reduction and obtain respective incentives in return. In this work, a game theory based dynamic pricing strategy is evaluated for Singapore electricity market, with focus on the residential and commercial sector. The proposed pricing model is tested with five load and price datasets to spread across all possible scenarios including weekdays, weekends, public holidays and the highest/lowest demand in the year. Three pricing strategies are evaluated and compared, namely, the half-hourly Real-Time Pricing (RTP), Time-of-Use (TOU) Pricing and Day-Night (DN) Pricing. The results demonstrate that RTP maximizes peak load reduction for the residential sector and commercial sector by 10% and 5%, respectively. Moreover, the profits are increased by 15.5% and 18.7%, respectively, while total load reduction is minimized to ensure a realistic scenario.
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This paper proposes a novel method to design feasible Time-of-Use (ToU) tariffs for domestic customers from flat rate tariffs by clustering techniques. The method is dedicated to designing the fundamental window patterns of ToU tariffs rather than optimising exact prices for each settlement period. It makes use of Gaussian Mixture Model clustering technique to group half-hour interval flat rate tariffs within a day into clusters to determine ToU tariffs. Two groups of ToU are designed following the variations in energy prices and system loading demand respectively. With a number of price-oriented and load-oriented ToU tariffs, the investigation is further carried out to explore the effects of these ToU tariffs on domestic demand response (DR), especially in terms of energy cost reduction and peak shaving. The DR in this paper is assumed to be enabled by household storage battery and the objective of the DR in response to each ToU tariff is to minimise the electricity bills for end customers and/or mitigate network pressures. An example study in the UK case is also carried out to demonstrate the effectiveness of the proposed methods.
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Introduction, 585. — I. Solution of the two period peak load problem under simplifying assumptions, 587. — II. Relation of the solution to some earlier contributions, 592. — III. Can purely cost-based prices be used to achieve optimal results?, 596. — IV. Some policy implications, 602. — Mathematical Appendix, 604.
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Book
Nonlinear pricing is pricing that is strictly proportional to the quantity purchased. For example, railroad tariffs are often based on weight, volume, and distance to be shipped; airlines offer frequent flyer bonuses based on miles flown; electric utilities charge different rates for different amounts of electricity used combined with the time it is used based on peak power demands. The book is divided into two parts. The first in a non-mathematical discussion of nonlinear pricing and the second part is more technical and is intended for readers interested in advanced topics.
Dynamic residential load scheduling based on adaptive consumption level pricing scheme
  • H T Haider
  • O H See
  • W Elmcnreich