2016 30th International Conference on Advanced Information Networking and Applications Workshops
Cost and Load Reduction using Heuristic Algorithms in Smart Grid Zafar Iqbal1 , Nadeem Javaid2,∗ , Mobushir Riaz Khan1 , Imran Ahmed3 , Zahoor Ali Khan4,5 , Umar Qasim6 1 Pir
Mehr Ali Shah Arid Agriculture University, Rawalpind 46000, Pakistan Institute of Information Technology, Islamabad 44000, Pakistan 3 Institute of Management Sciences (IMS), Peshawar 25000, Pakistan 4 Faculty of Engineering, Dalhousie University, Halifax, NS, B3J 4R2 Canada 5 Higher Colleges of Technology, Fujairah 4114, United Arab Emirates 6 University of Alberta Edmonton, Alberta T6G 2J8, Canada ∗ Corresponding author:
[email protected], www.njavaid.com 2 COMSATS
Abstract—Due to smart grid applications the consumers and producers are able to meet the demand of each others and thus take part in demand side management and demand response program. Hence smart grid leads to optimization of energy consumption and reduce high cost in today extensive demand of energy. In this research work we are reducing electricity consumption cost and load consumption using scheduling the appliances. The twenty appliances are used to schedule their energy consumption and load using heuristics techniques i.e. binary particle optimization, genetic algorithm and wind driven optimization, using the same data set for each technique and their results are compared with each other in order to find which technique do better optimization. Simulations are performed in matlab to show the cost and load reduction by the above three techniques and validate the experiment. The simulation results show that binary particle swarm optimization perform better than the other two techniques and wind driven optimization is better than genetic algorithm but not able to perform as binary particle swarm optimization, similarly genetic algorithm is least efficient as compared to both methods. Our research work is beneficial to meet the demand side management and help in reducing electricity cost and load for consumers.
(2) Flexibility in network topology, its network topology is very flexible and it allow bidirectional information flow, (3) Efficiency, it provides flexibility in implementation of different demand side management and demand response programs to manage end user energy demand, (4) Peak curtailment, as the prices of electricity changes throughout the day due to energy demand variations and there are chances of high peaks during low pricing hours which can disturb the stability of electric grid. Through the implementation of different energy management techniques, load can be scheduled in order to avoid high peaks, (5) Stainability, it has the ability to sustain even if greater amount of other distributed power sources penetrate like solar power, wind power etc, (6) Market enabling, through mart metering infrastructure, smart grid provides greater flexibility to suppliers and consumers to sell extra energy back to grid, (7) Demand response, smart grid provides support to consumers in the form of demand response programs, where supplier or generators can estimate the end user energy demands at particular time slots to manage electricity generation. (8) Platform for advance services i.e. it also provides some advance services like fire monitoring and alarm which can shut off power system and emergency call service [2]. Now a days energy crises are everywhere and the expensive energy production mechanisms are harder to use in a manageable way. Along with energy demand management, SG minimizes the emission of greenhouse gases and make the chances of global warming less to occur. In this work we consider twenty appliances, their power rating, price signal, and length of operation time to evaluate the energy consumption and electricity bill. We have done cost and load optimization in this work,and have used three evolutionary and heuristic techniques to calculate cost and load for these appliances. These techniques are binary particle swarm optimization, genetic algorithms and wind driven optimization. We have evaluated the performance of each technique and compare their values with each other in order to find which
Keywords:-Appliance Scheduling, Demand Side Management, Demand Response Program, Cost Reduction, Load Reduction, Binary Particle Swarm Optimization, Wind Driven Optimization, Genetic Algorithm, Smart Grid. I. I NTRODUCTION The world is now moving toward a high technological era due to advancement in information and communication technologies. Among these technologies, Smart Grid (SG) is the advanced form of traditional grid due to incorporation of two way communication network to facilitate both end users and utility. SG is helpful in providing intelligent monitoring, control system, communication and self-healing services to consumers [1]. Smart grid have some general features which include; (1) Reliability, SG has the ability to cope with faults and provides self-healing mechanisms which shows its reliability and less vulnerability to some natural disaster or intruder attacks, 978-1-5090-2461-2/16 $31.00 © 2016 IEEE DOI 10.1109/WAINA.2016.160
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techniques perform better in term of cost and load optimization. For this purpose to calculate the exact values and find accurate comparison among these techniques we have taken the same power rating, same price signal, same time slots, length of operation time, same appliances bits pattern,same number of appliances and same values for all the parameters for all these twenty appliances and feed them to these three techniques one by one and generated the results and then these results are compared as shown in a table V and VI, in the form of scheduled and unscheduled cost and load, their total cost, total load reduction, their total percentage cost and percentage load reduction. If we see the table V and VI it is shown that Binary particle swarm optimization perform better than other two techniques. The rest of the paper is organize as follows.sectiob II describes the existing work,motivation of idea is in section III and section IV compare the optimization techniques used in this paper.Cost and load optimization is described in section V,and section Vi discusses the Simulation results,and finally section VII provides conclusion.
home energy management and appliance scheduling. They have taken six appliances for their test system and simulated them via matlab. They generated load and cost plots for scheduled and unscheduled cases. In [10] the authors use particle swarm optimization and binary particle swarm optimization for scheduling of different scenario as shown in Table.1,in their work to optimize electricity cost and reduce load consumption. They schedule DER with no battery storage, with battery storage, with net feed-in tariff and no battery storage, with net feed-in tariff and battery storage. They also used case study for energy service provisioning in a smart home, and thus via scheduling they achieve cost and load optimization. In [11] the authors use APSO a variant of PSO to schedule the Power consumption or charging of two PHEV-1 and PHEV-2.They have taken some scenarios for scheduling from Monday to Friday and shift the Peak load demand to off peak hours and thus reducing total energy consumption cost. They are using grid power, solar power for charging and ToU pricing scheme. They have calculated hourly unit power price, hourly solar power generation, hourly regular power demand, hourly power demand by PHEV-1, hourly power demand by PHEV-2, hourly combined power demand, hourly grid power demand, hourly grid power price through simulation and thus show optimization in cost via scheduling. In [12] the authors use genetic algorithm to optimize cost and scheduled appliances in a smart home. They schedule their appliances in the home for the purpose of reducing electricity expenses and to reduce Peak to Average Ratio (PAR).They have introduced general architecture of energy management system in a smart home and then propose an efficient scheduling method for home power usage. They have used RTP and IBR pricing model, by using this combine model their proposed scheduling strategy has efficiently reduced cost and peak to average ratio. In [13] the authors use genetic algorithm to find the optimum schedule arrangement for all the energy consumption by appliances in a smart home to reduce the electricity cost. They used intelligent task scheduling module to minimize the entire energy expense in a smart home, if the module could schedule the appliances start time. They used the genetic algorithm approach for minimizing the residential total electricity cost in demand response services. Their approach consider task or appliance on /off time constraints and the circuit maximum load constraints. Further they compared their approach with other two techniques i.e. SA and greedy method and showed via simulation that their technique has obtained optimal scheduling solution for residential customers as well as satisfying the equipments operation time constraints and the entire power load constraints. Hence their simulations prove that genetic algorithm can efficiently reduce electricity cost for residential customers.In [14] the authors in this work proposes NSGA-2 a genetic based algorithm to solve the optimization problem. The optimization problem is to reduce the total energy cost and peak to average ratio of the total energy demand. They have taken strict energy consumption scheduling and soft energy consumption scheduling for appliances and generate plots for scheduled energy consumption and their corresponding cost
II. R ELATED W ORK Some of the existing work related to our work are listed below. In [3] the authors are using binary particle swarm optimization for solving the optimization problem and reducing electricity bill. The authors proposing home energy management system with RES, without RES, with smart scheduler, without smart scheduler,with smart scheduler plus RES and conventional system. The consumers are classified into three classes i.e. conventional users, smart users and prosumers, scheduling is performed via BPSO technique to reduce cost and load. In [4] the authors divide the appliances into categories and proposing an optimal scheduling algorithm and using binary particle swarm optimization technique to reduce electricity cost. They consider supplier and consumer scenario also with renewable energy resources. They consider the personal habits and characteristics of appliances and draw a table of time of use electricity prices for appliances operation with the objective to reduce cost. When the power demand is high or low the supplier will get a signal and will ask the consumers to on/off some appliances or switch off some high power consuming appliances so they will be able to achieve load shifting and thus avoid peak hours cost.In [5] the authors use particle swarm optimization for scheduling the operation of distributed energy resources. They use decision support tool to optimize energy services of residential end users. In [6] the authors used PSO and DER for scheduling the energy services, they use different cases for scheduling and optimizes cost. They also use case study and decision support tool to optimize energy services to smart homes.In [7] the authors present a scheduling strategy for cost optimization and demand response management using particle swarm optimization technique to simulate their different scheduling scenarios. In [8] the authors use non cooperative game theoretic model for cost optimization using scheduling during power outages. In [9] the authors use binary particle swarm optimization for
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results are then compared as shown in Table.2. And explained and analyzed in section simulation results and discussion.
without optimization and scheduled energy consumption and corresponding cost with optimization and thus reduces total consumption cost and peak to average ratio. In [15] the authors used genetic algorithms for the retailers to maximize the profit. They have scheduled the appliances and the retailer use the appliance scheduling information to maximize its profit by solving the profit maximization problem. They have also used other techniques besides i.e. Stackelberg Game Approach and linear programming approach besides genetic algorithm maximize profit and reduce cost and peak to average ratio. They have divided 24 hours time slot in to three groups i.e. on-peak hours (5 PM-12 PM), mid-peak hours (8 AM-5 PM) and off-peak hours (12 PM-8 AM).
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III. M OTIVATION
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Energy crises and problems are everywhere in the world and increasing day by day, Hence experts, scientists and researcher are constantly trying to develop and explore new ways of energy production and safe utilization. Researchers and scientists are trying to develop such ways to minimize Energy wastage and also provide less expensive and smart energy resources with a minimum customer cost. Hence costly energy production and costly distribution to consumers as well as wastage of this costly energy is a big problem these days for every country unless they did not formulate a special strategy or design a special solution to mitigate these issues. Hence smart grid is one such solution of the many existing solutions for providing less expensive energy to consumers, preventing wastage of energy and smart utilization and smart distribution of energy is provided. In short SG means computerizing the electric grid or conventional power grid into smart one, so it can perform smart functions like minimize wastage of energy during distribution to consumers, reduce electricity cost, maximize user comfort, minimize user frustration level. The customers are always novice users and are not aware of demand side management or DR program so they are unable to use energy according to their use and thus wastage of energy occurs and they always pay more cost what they have used. Hence by the use of smart grid the above issue can be solved or minimize to a maximum extent. In this work we are taking the similar motivation of electricity cost, electricity bill reduction and load reduction. We are using three biologically inspired evolutionary techniques in our work for electricity cost reduction and load optimization and reduction. i.e. binary particle swarm optimization (BPSO), genetic algorithms (GA), and wind driven optimization (WDO).These three optimization techniques are used to optimize or reduce electricity bill and electricity load i.e. to generate scheduled and unscheduled cost, scheduled and unscheduled Load. For this purpose similar data set i.e. total twenty appliances which consume wattage above five hundreds watts, having same price signal, same power rating, and having the same time slots and length of operation time (LOT) are taken for each of the above three techniques mentioned in Table.2, to generate scheduled and unscheduled cost and load results, ToU pricing, and these
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IV. C OST AND L OAD OPTIMIZATION Cost Reduction:-The purpose of our work is to achieve cost and load optimization using evolutionary techniques. These techniques have reduced the cost and load successfully. Objectives:-The objective of our work is to reduce electricity bill and load consumption and as the energy these days are very costly and if we switch on our appliances during high peak hours then it will add extra cost to our bill. We have used three evolutionary techniques to reduce electricity cost using the same data set for all appliances. The efficiency of the three techniques are described below. 1. Most Efficient Technique.Binary particle swarm optimization is and evolutionary and heuristics technique which is used in many fields for optimization. Kennedy and Ebarhart in 1995 developed the particle swam algorithm by studying the social and cognitive behavior of ants. The individuals or objects called particles are flown through a multidimensional search space [16]. The scheduled cost and unscheduled cost generated by BPSO for twenty appliances are Rs 1454.8 and Rs 2341.6 respectively, and the total reduction in cost Rs 886.8 and total percentage reduction in cost is 37.88% as shown in Table I. Hence BPSO cost reduction is more than the other two techniques so it show best performance as compared to other two techniques. 2. Efficient Technique. The wind driven optimization was initially developed by Dr. Zikri Bayraktar during his graduate studies at the Pennsylvania State University. The WDO is a novel nature-inspired global optimization algorithm based on atmospheric motion. It is a population based iterative heuristic global optimization algorithm for multi-dimensional and multi-model problems having ability to implement constraints on the search domain. A population of very small air particles navigate over an N-dimensional search space using second law of motion, which is also used to explain the motion of air parcels within the earth atmosphere.WDO add extra
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TABLE I C OST R EDUCTION Cost Category Scheduled Cost Unscheduled Cost Reduction in Cost %age Reduction in Cost
BPSO Rs 1454.8 Rs 2341.6 Rs 886.8 37.88%
TABLE III L OAD R EDUCTION
WDO Rs 1827.8 Rs 2288.1 Rs 460.3 20.2%
GA Rs 2077.8 Rs 2341.6 Rs 263.8 11.26%
Load Category Scheduled Load Unscheduled Load Reduction in Load %age Reduction in Load
Method#2 837 877 25 144 300
GA 153.2 kwh 153.2 kwh 00.0 kwh 00.0 kwh
V. S IMULATION R ESULTS AND D ISCUSSION
TABLE II N ONLINEAR M ODEL R ESULTS Method#1 50 47 31 35 45
WDO 148.2 kwh 148.2 kwh 00.0 kwh 00.0%
is uniform and consumption is balanced by consumers there will be no high peaks and hence the peak to average ratio will be small.It is the requirements of demand side management and demand response program that peaks should be balanced and the PAR should be less. 1. Most Efficient Techniques.As binary particle swarm optimization is explained above. We have used BPSO for load reduction. The scheduled and unscheduled load by BPSO are 118.0 Kwh and 153.2 kWh respectively and their difference is 35.2 kWh which is actually reduction in load. The percentage reduction in load by BPSO is 22.98% which show that BPSO is better than the other two techniques, as shown in Table II. 2. Efficient Techniques. (WDO) wind driven optimization as explained above are used for load reduction. The scheduled and unscheduled load by wind driven optimization are 148.2 kWh and 148.2 kWh respectively and their difference is 00.0 Kwh which is actually load reduction. The percentage load reduction by wind driven optimization is 00.0%. Hence wind driven optimization is better than GA but not than BPSO in our case as shown in Table II. 3. Least Efficient Techniques. Genetic algorithm as explained above is used for load optimization in our work. The scheduled and unscheduled load by genetic algorithm is 153.2 kWh and 153.2kwh respectively and their difference is 00.0% which is reduction in load. The percentage reduction in load is also 00.0% which is similar to wind driven optimization. Hence in this case the genetic algorithm shows least performance as shown in Table II.
term in velocity update equation i.e. gravitation and Coriolis forces which provides robustness and extra degree of freedom to fine tune the optimization. They claim that WDO can perform better than PSO and that it is well-suited for problems with both discrete and continuous-value parameters [17]. The scheduled and unscheduled cost by WDO in our work is Rs 1827.8 and Rs 2288.1 and the total reduction in cost is Rs 460.3 and the percentage reduction in cost is 20.2%, which show enough reduction in cost by WDO and as they claimed but our simulation via Matlab show that WDO cost reduction is less than BPSO as BPSO is 37.88% and WDO is 20.2%. Hence in our scenario WDO is least efficient than BPSO as shown in Table I. 3. Least Efficient Technique.Genetic algorithms were initially developed by John Holland in early 1970.Genetic algorithm were developed to show some behavioral processes observed in natural evolution. The idea with GA is to use this power of evolution to solve optimization problems. we have used genetic algorithm to optimize cost and load using scheduling. We have taken twenty appliances and calculate their scheduled and unscheduled cost. The scheduled and unscheduled cost are Rs 2077.8 and Rs 2341.6 respectively, and their difference is Rs 263.8 which is actually reduction in cast after we run the scheduled load. The percentage reduction in cast by GA is 11.26% which in our scenario is less from both BPSO and WDO as shown in Table V below. Hence GA in our scenario is least efficient in cost reduction as compared to two other techniques.
Case 1 2 3 4 5
BPSO 118.0 kwh 153.2 kwh 35.2 kwh 22.98%
In this paper the three techniques discussed above are used for which plots are explained and analyzed below. The ToU pricing scheme plot is shown in fig.3 which show cost in rupees and time slots in hours. The time slots curve ranging from 10 to 20 for cost and 0 to 25 on x-axis for time slots. The time of use pricing scheme is dynamic and its price depends on time slots.It usually includes pricing depending on very low peak hours, low peak hours, very high peak hours, high peak hours, medium peak hours, and shoulder peak hours. fig.4 show bar graph for scheduled and unscheduled load and cost. The red bar show unscheduled load and unscheduled cost which are 153.2 kWh and Rs 2341.6 and the blue bar show scheduled load and scheduled cost which are 118.0 Kwh and Rs 1454.8 respectively as shown in Table V and VI.Hence the difference between scheduled and unscheduled load and scheduled and unscheduled cost is Rs 886.8 and 35.3 kwh respectively as shown in fig.4 and Table V and VI,which show a great reduction in load and cost as evident from the
Method#3 970 230 415 2356 556
Load Reduction:-The second goal was to reduce or balance load consumption by consumers and end users so that the peak to average ratio could be maintained and there should be balance load on the utility company and the consumers will not have any problem from utility side, and demand side management purpose will be achieved. Objectives:-In this work our objectives was to optimize cost and load consumption using below three evolutionary algorithms and then compare the resulted values with each other to find out which techniques perform best. If the load distribution
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Fig. 2. Electricity cost and energy consumption comparison of BPSO algorithm
bar graph. This reduction in load and cost after scheduling is much greater than the other two techniques as shown in Table V and VI and figure 4. Percentage reduction in cost is 37.88% and percentage reduction in load is 22.98% as can be seen in bar graph and Table V and VI.37.88% reduction in cost and 22.98% reduction in load for BPSO show that this optimization techniques perform much better than the other two techniques as can be seen in Table V and VI. And fig.1,fig.2,fig.3,fig.4. The second optimization techniques used in our work is genetic algorithm.fig.5 show the scheduled and unscheduled cost curve. The red line is scheduled cost and blue line is unscheduled cost.The time slots are taken in hours and electricity cost are taken in rupees. The blue line or unscheduled cost have only one sharp peak which show the peak or maximum load at that time slot. The total scheduled and unscheduled cost are Rs 2077.8 and Rs 2341.6 which shows that unscheduled cost is much more than scheduled cost and the difference between scheduled and unscheduled cost is Rs 263.8 which is actually total reduction in cost. The percentage reduction in cost is 11.26% by GA which is less than 1/3 of the BPSO as shown in Table .V and VI.It show that for our same data set and parameters GA perform very poor as compared with BPSO as the percentage cost reduction can be seen in Table V.
curve as for BPSO.The cost are shown in rupees and time slots are shown in hours and the curve ranging from 15 to 20 on y-axis and from 0 to 25 on the x-axis which show total 24 hours time slots. fig.8 shows the bar graph of scheduled and unscheduled load and cost for genetic algorithm. The cost are shown as Rs/hour, the red bar show scheduled load and cost and blue bar show unscheduled load and cost. The scheduled load and cost of GA are 153.2 kWh and Rs 2077.8 respectively while unscheduled load and cost are 153.2 kwh and Rs 2341.6 respectively, which show that scheduled and unscheduled load of GA are same while cost have Rs 263.8 difference which is actually total reduction in cost by GA which is less than 1/3 of the cost reduction by BPSO.Its percentage reduction in cost is 11.26% and load is zero percent, which is significantly less as compared to 37.88% of BPSO as shown in Table .V and VI and fig.8. fig.9 show scheduled and unscheduled cost curve. Time slots is shown in hours and cost in rupees. scheduled cost is shown by blue line and unscheduled cost by red line. The peaks are shown as in fig.9.The scheduled and unscheduled cost are Rs 1827.8 and Rs 2288.1 respectively and their difference is Rs 460.3 which is actually total reduction in cost by WDO.The percentage reduction in cost by WDO is 20.2%,which is less than BPSO but better than GA.Hence WDO perform better than GA for cost reduction. fig.10 show load curve for scheduled and unscheduled load. energy consumption is shown in watts and time slots are shown in hours. Both the curve have very low peaks. The red line show unscheduled load and black line show scheduled load. The scheduled and unscheduled load for WDO are 148.2 kWh and 148.2 kWh respectively which are similar hence their difference is zero and same is reduction in load.WDO reduce cost 20% but load reduction is zero percent which is similar to GA. fig.11 show the plot of ToU pricing scheme for WDO which show time slots in hours and cost in cents/kwh.The Total time slots are 24 i.e. from 0 to 25.The wind driven optimization ToU signal is similar to BPSO and GA and varies between Rs
In fig.6 scheduled and unscheduled load curve are shown.The red line show scheduled load and blue line show unscheduled load. energy consumption is shown in Kwh and time slots in hours. The blue line i.e. unscheduled load show only one sharp peak and the rest of the curve are smooth. The total scheduled and unscheduled load calculated by GA are 153.2 kWh and 153.2 kWh respectively which are similar and hence their difference will be 00.0 kWh and so the total reduction in cost will also be Rs 00.0 and the percentage reduction in load is also zero as shown in table VI.Hence GA total load in our scenario is zero as compared to BPSO. fig.7 show the ToU pricing scheme for GA, which show similar
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Fig. 3. Electricity cost and energy consumption comparison of GA
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optimization, using twenty appliances and using the same data set i.e. power rating, electricity price signal, time slots and length of operation time for the three techniques and obtained different results by each, as the energy optimization is a very critical issue these days, our purpose of this paper is to minimize and reduce expensive energy cost and balances the load consumption by residential consumers. We have also compared the results of scheduled and unscheduled cost, load, total reduction in cost, load and their percentage reduction in cost and load by these techniques and found that which techniques perform best for cost and load optimization. Our simulation results and table V and VI show the total reduction in cost and load and also show that BPSO perform the best as compared to other two techniques. The %age cost reduction by BPSO is 37.88%, WDO 20.2% and GA is 11.26%.our simulation results show that we have achieved cost reduction, and BPSO is best as its reduction is 37.88%. In future we intend to use these techniques for more optimized values to
10 and Rs 20. fig.12 show the bar graph of scheduled and unscheduled load and cost. The cost are shown in rupees. The red bar show unscheduled load and blue bar show scheduled load. scheduled and unscheduled cost by WDO are Rs 1827.8 and Rs 2288.1 respectively which show the difference of Rs 460.3 which is reduction in cost and the percentage reduction is 20.2%,as shown in bar graph and Table.V and VI.The scheduled and unscheduled load by WDO are 148.2 kwh and 148.2 kwh which is same. All simulation results and discussion show that BPSO is best than GA and WDO in cost and load reduction while GA perform much poor than the other two techniques as seen in all the figures and Table V and VI. VI. C ONCLUSION In this work we have used evolutionary and heuristics techniques i.e. binary particle swarm optimization, genetic algorithm and wind driven optimization for cost and load
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obtain and also reduce PAR value, user comfort maximization and energy minimization.
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