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Rabiya Khalid
added 2 research items
In domestic area, demand of an electricity has been growing with the increase of energy consumed by appliances. So, there must be a mechanism for scheduling the appliances and reducing a power consumption in Home Energy Management System (HEMS). In this regard, we integrate two heuristic techniques Genetic Algorithm (GA) and Biography Based Optimization (BBO) in HEMS by using smart grid. Our discussion and simulations results clearly shows the effect on cost minimization, peak to average reduction and load reduction from on-peak to off- peak hours. We have used a Critical Peak Pricing (CPP) model for electricity bill calculation. Both GA and BBO outperforms for the reduction of cost Peak to Average Ratio (PAR) and load, by achieving user comfort maximization.
This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load that is difficult to process with conventional computational models, referred as big data. The processing and analyzing of big data divulges the deeper insights that help experts in improvement of smart grid operations. Processing and extracting of the meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand on big data using deeper Long Short-Term Memory (LSTM). Due to adaptive and automatic feature learning of DNNs, processing of big data is easier with LSTM as compared to purely data driven methods. The proposed model is evaluated using a well-known real electricity markets’ data.
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Demand Side Management (DSM) is one of the most important aspects in a smart grid towards energy efficiency. By adopting DSM, the users inform the power utility about their power consumption profile. The power utility dispatches the real time prices, according to the users’ demand. The DSM greatly affects the cost of individual users as well as the per unit generation cost of electricity by avoiding the use of oil and gas based peak power plants. The minimal use of peak power plants also reduces the environmental pollution. In literature, several DSM techniques have been proposed. The main focus of these techniques is to reduce the total electricity cost and Peak-to-Average Ratio (PAR). However, these techniques consider a limited number of appliances, limited number of users and problems with their proposed billing mechanism. The main objective of this research work is to develop a Generic Demand Side Management (G-DSM) model for residential users to reduce the PAR, the total energy cost, Waiting Time of Appliances (WTA), and to achieve fast execution of the proposed model. The G-DSM model is based on Genetic Algorithm (GA) for appliance scheduling. In this model, we have proposed a complete system architecture and also a mathematical formulation for total energy cost minimization, PAR reduction, and the WTA. With the G-DSM model, we have considered 20 users and 23 appliances having different operational characteristics. MATLAB/Simulink with Stateflow and optimization toolbox is used as a simulation tool for G-D M model implementation. Simulation results show that the G-DSM model minimizes energy cost by 39.39% and reduces PAR by 17.17% of a single user. In case of multiple users, the total energy cost minimization and PAR reduction are 45.85% and 52.24%, respectively.
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This dissertation explores and identifies that home energy management systems (HEMSs) are used to implement demand side management in homes. Based on integration of renewable energy sources (RESs) and energy storage systems (ESSs), HEMS operation (HEMO) is classified into demand response (DR) and DR synergized with RESs and ESSs optimal dispatch (DRSREOD). DR-based HEMO depends on shifting of the consumer load towards off-peak times. DRSREOD-based HEMS benefits the consumer and the utility by reducing the cost of generation, reducing energy bills, minimizing green house gas (GHG) emissions, achieving overall energy savings and increasing energy sustainability. The contributions in this dissertation are three fold. First, this dissertation reviews the most recent literature on various models for DRSREOD-based HEMO. The reviewed models for HEMO are classified into dichotomous approaches as DR versus DRSREOD-based individual versus coordinated, deterministic versus stochastic, single-objective versus multi-objective and conventional techniques versus advanced heuristics-based. In addition, the tradeoffs among the dichotomous approaches, challenges pertinent to coordination and eminent issues related to standardization requirements for modeling home appliances (HAs) are investigated. Second, an improved algorithm for a DRSREOD-based HEMS is then proposed in this dissertation. This heuristic-based algorithm considers DR, photovoltaic (PV) availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power sources to supply the required load. The HEMS problem has been solved to minimize the cost of energy (CE) and time-based discomfort (T BD) with conflicting tradeoffs. The mixed scheduling of appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the CE and T BD performance parameters using an inclining block rate (IBR) pricing scheme. A set of optimized tradeoffs between CE and T BD has been computed to address multiobjectivity using a multi-objective genetic algorithm with pareto optimization to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Third, a drastically rising demand of electricity has forced a number of utilities in developing countries to impose large-scale load shedding (LS). A HEMS based on DRSREOD integrated with an LS-compensating dispatchable generator (LDG) (DRSREODLDG) ensures an uninterrupted supply of power for the consumers subjected to LS. The LDG operation to compensate the interrupted supply of power during the LS hours; however, accompanies the release of GHGs emissions as well that need to be minimized to conserve the environment. A 3-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of DRSREODLDG-based HEMS. The method provides the tradeoffs between the net cost of energy (CEnet) to be paid by the consumer, the T BD due to shifting of HAs to participate in the HEMS operation and minimal emissions (T EM iss) from the local LDG. At step-1, primary tradeoffs for CEnet, T BD and T EM iss are generated through a heuristic that takes into account PVs availability, the state of charge and the related rates for the storage system, mixed shifting of HAs, IBR, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. At step-2, a constraint filter based on the average value of T EM iss is used to filter out the tradeoffs with extremely high values of T EM iss. At step-3, a constraint filter made up of an average surface fit for T EM iss is applied to screen out the tradeoffs with marginally high values of T EM iss. The selected solutions are classified for critical tradeoff analysis to enable the consumer by choosing the best option from a diverse set of eco-efficient tradeoffs between CEnet, T BD and T EM iss. Finally, this thesis focuses on decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach which is proposed for optimal operations of price-driven DR (PDDR) and PDDR- synergized with the renewable and energy storage dispatch (PDDR-RED) based HEMSs. Simulation results show the effectiveness of all the proposed schemes in comparison to the previous schemes.
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A smart city is an efficient, reliable, and sustainable urban center that facilitates its inhabitants with a high quality of life standards via optimal management of its resources. Energy management of smart homes (SHs) is one of the most challenging and demanding issues which needs significant effort and attention. Demand side management (DSM) in smart grid (SG) 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. In DSM, scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller (HEMC). However, user discomfort is caused by the scheduling of home appliances based on the demand response or limiting its time of use. Further, rebound peaks that are regenerated in the off-peak hours is also a major challenge in DSM. In addition, an increase in the world population is resulting in high energy demand; thus, causing a huge consumption of fossil fuels. This ultimately results in severe environmental problems for mankind and nature. Renewable energy sources (RESs) emerge as an alternative to the fossil sources. These RESs have the advantages of environmental friendliness and sustainability, which are incorporated in SHs via two modes: grid-connected (GC) or stand-alone (SA). The reliability concerns in RESs are usually met with the usage of hybrid RESs along with the integration of energy storage systems (ESS). The efficient usage of these components in the hybrid RESs requires an optimum unit sizing that achieves the objectives pertaining to cost minimization and reliability in SA mode. These are some of the main concerns of a decision maker. This thesis focuses on employing meta-heuristic techniques for efficient utilization of energy and RESs in SH. At first, an evolutionary accretive comfort algorithm (EACA) is developed based on four postulations which allows the time-varying priorities to be quantified in time and device-based features. Based on the input data, considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index (χ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of 1.5 $/day, 2.0 $/day, and 2.5$/day are performed. Secondly, a priority-induced DSM strategy based on the load shifting technique considering various energy cycles of an appliance is presented. The day-ahead load shifting technique is mathematically formulated and mapped with multiple knapsack problem (MKP) to mitigate the rebound peaks. The proposed autonomous HEMC embeds three meta-heuristic optimization techniques: genetic algorithm (GA), enhanced differential evolution (EDE), and binary particle swarm optimization (BPSO) along with the optimal stopping rule, which is used for solving the load shifting problem. Next, we integrate the RESs and ESS in a residential sector considering GC mode. The proposed optimized home energy management system minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of the electricity market. Here the appliances are classified into shiftable and non-shiftable categories, and a hybrid GA-BPSO (HGPO) scheme outperforms to other algorithms in terms of cost and a peak-to-average ratio (PAR). Finally, meta-heuristic schemes that do not depend on algorithmic-specific parameters are focused for RESs and ESS integration in a SA system. Preliminary, Jaya algorithm is used for finding an optimal unit sizing of RESs components, including photovoltaic (PV) panels, wind turbines (WTs), and fuel cell (FC) with an objective to reduce the consumer total annual cost. The methodology is applied to real solar irradiation and wind speed data taken for Hawksbay, Pakistan. Next, an improved Jaya and the learning phase as depicted in teaching learningbased optimization (TLBO), named JLBO algorithm for optimal unit sizing of a PV-WT-Battery hybrid system is also demonstrated for another site located in Rafsanjan, Iran. The system reliability is considered using the maximum allowable loss of power supply probability (LPSPmax) provided by the consumer. Thus, the thesis objectives achieved are to have a green, reliable, economical, and sustainable power supply in the SH.
Private Profile
added 8 research items
Modern power systems are being transformed towards distributed energy resources. Energy management in Smart Grid (SG) becomes challenging in the presence of Renewable Energy Resources (RERs) as they do not generate energy in deterministic manners. During off peak hours over-generation from RERs becomes the basis of renowned “duck curve” problem which causes the generator unit to be underloaded. Generator underloading impacts individual power system components as well as overall system performance due to generation-demand mismatch. The conceptofDemandDispatch(DD)alongwithEnergyStorage System (ESS) is considered as an effective tool to enhance the flexibility of demand side for mitigation of generationdemand imbalance. DD is believed to be power flexible loads specific, where load follows the generation. In this paper, radial structure of distribution grid along with commonly used configuration topology of building integration with RERs and ESS is considered. This paper addresses a multilevel Multi Agent System (MAS) optimization framework based on Particle Swarm Optimization (PSO) for optimal co-scheduling of electricity demand and supply resources under time of use pricing scheme. The MAS structure allows Plug and Play (PnP) capabilities and adaptively control the distributed energy resources in order to accomplish the goal of load balancing. During the peak and off-peak hours, PnP algorithm activates/deactivates the ESS to rectify energy mismatch. ESS stores the excess RERs energy and using it at suitable time interval to meet local demand. In this research work, it is intended to minimize the building electricity cost with adequate user comfort in peak hours and to maximize the building load penetration to cater load balancing during off-peak hours. Simulation results show that proposed MAS helps to maintain load balancing without compromising the user comfort.
In a transmission network, optimal power flow (OPF) is considered as one of the most widely studied non-linear, non-convex and highly constrained problem. While solving the conventional OPF problem, power generation system mainly consists of fossil fuel thermal generators; however, with the increased energy demand, renewable energy sources like wind turbines, solar photovoltaic panels and hydro plants are also introduced. OPF problem is solved using traditional and heuristic approaches to attain the stated objectives that mainly include fuel cost reduction, power loss minimization and emission reduction. These objectives are either optimized individually or in combination where two or more objectives are optimized simultaneously to achieve multi-objective optimization. Further, this study gives an overview of how these economical, environmental and technical objectives are achieved.
Rabiya Khalid
added 2 research items
The electricity demand from residential buildings is increasing gradually day by day. Home Energy Management Systems (HEMS) are used to meet this demand by using Demand Sides Management (DSM) to reduce the pressure on consumers and utility companies. In this paper, HEMS is facilitated by using different meta-heuristic scheduling techniques: The Strawberry Algorithm (SBA) and Bacterial Foraging Algorithm (BFA). The SBA is useful for every kind of optimization problem and helps in scheduling the electricity load. To compute the cost efficiently Time-of-Use (ToU) pricing scheme is used. Results illustrate that the cost is reduced efficiently along with Peak to Average Ratio (PAR).
The rise of energy demand is an alarming situation for mankind as it can lead towards a crisis. This problem can be easily tackled by assimilating Demand Side Management (DSM) with traditional grid by means of bi-directional communication between utility companies and consumers. This study evaluates the performance of Home Energy Management System (HEMS) using meta-heuristic optimization techniques: Genetic Algorithm (GA) and Crow Search Algorithm (CSA). The appliances are classified in three sets on the basis of their electrical energy consumption pattern. Moreover, the Real Time Pricing (RTP) scheme is used for power bill control. The core aims of this paper are to minimize electrical energy cost and consumption by scheduling of appliances, decline in peak to average ratio, while getting the best out of user comfort. Besides, simulation results illustrate that there is a trade-off between waiting time and electricity cost. The outcomes also indicate that CSA perform better as compared to GA in relation to cost.
Adnan Ishaq
added 2 research items
With the emergence of Smart Grid, users adopt different scheduling methods to reduce their energy consumption with different objectives. In this paper, we implemented a Meta heuristic techniques named as Grey Wolf Optimizer (GWO) and Bacterial Foraging Algorithm (BFA) for Home Energy Management System (HEMS). We implemented these techniques due to the inspiration from the working behavior of GW and B. In GW, wolves are categorized into four forms namely Alpha, Beta, Delta and Omega. There are three major steps in GW for getting their prey which are first searching the prey then encircling the prey and finally attacking the prey. We proposed a generic architecture of Demand Side management (DSM) that incorporates residential area domain. We use Day Ahead Pricing (DAP) to calculate the cost of energy consumption. Results are compared with the BFA. Results show that GWO has better performance as compared to the other Meta heuristic technique BFA. GWO effectively reduces the cost of energy consumption as compared to BFA. Therefore implementation of this technique is useful for both users and utility.
Electricity is a controllable and convenient form of energy. In this paper we discus about the electricity control. In current years Demand Side Management (DSM) techniques are designed. For residential and commercial sectors. These techniques are very effective to control the load profile of customer in grid area network. In this paper we use two optimization techniques: Harmony Search Algorithm (HSA) and Firefly Algorithm (FA).In our work we categorize smart appliances in three different categories on the basis of their energy consumption. For energy pricing we use Time of Use (ToU)pricing signal.Simulation result verify our adopted approach significantly reduce the cost without compromise the user comfort.
Adnan Ishaq
added 6 research items
Nowadays, Energy become the most valued necessity. Energy crisis becomes a critical issue of this era. Energy demand is increasing day by day, due to which peak load creation occurs. In order to handle the critical situation of the energy crisis, many techniques and methods are implemented. This can be done by replacing the traditional grid with smart grid and scheduling of appliances at Demand Side Management (DSM). Our main focus is on load management and minimization of cost which can be done by load shifting from on peak hours to off peak hours. We have achieved objectives by using two meta-heuristic optimization techniques; Harmony Search Algorithm (HSA) and EarthWorm optimization Algorithm (EWA). Simulation results show that the approaches we adopted reduce the cost, reduce the Peak Average Ratio (PAR) by load shifting from on peak to off peak hours between the min and max interval with a low difference.
With the emergence of Smart Grid, users adopt different scheduling methods to reduce their energy consumption with different objectives. In this paper, we implemented a Meta heuristic techniques named as Grey Wolf Optimizer (GWO) and Bacterial Foraging Algorithm (BFA) for Home Energy Management System (HEMS). We implemented these techniques due to the inspiration from the working behavior of GW and B. In GW, wolves are categorized into four forms namely Alpha, Beta, Delta and Omega. There are three major steps in GW for getting their prey which are first searching the prey then encircling the prey and finally attacking the prey. We proposed a generic architecture of Demand Side management (DSM) that incorporates residential area domain. We use Day Ahead Pricing (DAP) to calculate the cost of energy consumption. Results are compared with the BFA. Results show that GWO has better performance as compared to the other Meta heuristic technique BFA. GWO effectively reduces the cost of energy consumption as compared to BFA. Therefore implementation of this technique is useful for both users and utility.