METAHEURISTICS FOR ENERGY SYSTEM PLANNING AND OPERATION
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TITLE PAGE
BY
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--/H2013/01430
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SCHOOL OF ---
INSTITUTE OF ---
DECEMBER,2018
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This is to certify that the research work, "metaheuristics for energy system planning and operation" by ---, Reg. No. --/H2007/01430 submitted in partial fulfillment of the requirement award of a Higher National Diploma on --- has been approved.
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Supervisor Head of Department.
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DEDICATION
This project is dedicated to Almighty God for his protection, kindness, strength over my life throughout the period and also to my --- for his financial support and moral care towards me.Also to my mentor --- for her academic advice she often gives to me. May Almighty God shield them from the peril of this world and bless their entire endeavour Amen.
ACKNOWLEDGEMENT
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Smart grid (SG) provides a prodigious opportunity to turn traditional energy infrastructure into a new era of reliability, sustainability and robustness. The outcome of new infrastructure contributes to technology improvements, environmental health, grid stability, energy saving programs and optimal economy as well. One of the most significant aspects of SG is home energy management system (HEMS) – which involves system planning and operation. It encourages utilities to participate in demand side management programs to enhance efficiency of power generation system and residential consumers to execute demand response programs in reducing electricity cost. This paper presents HEMS on consumer side and formulates an optimization problem to reduce energy consumption, electricity payment, peak load demand, and maximize user comfort. For efficient scheduling of household appliances, we classify appliances into two types on the basis of their energy consumption pattern. In this paper, a meta-heuristic firefly algorithm is deployed to solve our planning, operation and optimization problem under real time pricing environment. Simulation results signify the proposed system in reducing electricity cost and alleviating peak to average ratio.
LIST OF ABBREVIATION AND THEIR MEANING
Home Energy Management Systems (HEMS)
Peak to Average power Ratio (PAR)
Operational start time (OST)
Operational time interval (OTI), length of operational time (LOT)
And thermal energy storage (TES)
Demand side management (DSM)
Demand response (DR)
Demand side management (DSM)
Integer linear programming (MILP)
Heuristic evolutionary algorithm (EA)
Control algorithm (CA)
Energy storage system (ESS)
Simulated annealing (SA)
Variable neighborhood search (VNS)
Guided local search (GLS)
Energy management controller (EMC)
Time of use (TOU)
Ant colony optimization (ACO)
Genetic algorithm (GA)
Variable neighborhood search (VNS)
Particle swarm optimization (PSO)
COVER PAGE
II. CERTIFICATION
III. DEDICATION
IV. ACKNOWLEDGEMENT
V. ABSTRACT:
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the study
1.2 Statement of the problem
1.3 Aim and objectives of study
1.4 Significance of the study
1.5 Scope and limitation of the study
1.6 Organization of the study
CHAPTER TWO
LITERATURE REVIEW
2.1 Review of the Study
2.2 Review of related studies
2.3 Overview of metaheuristic
2.4 Classification of metaheuristics
CHAPTER THREE
METHOD
3.1 Problem Formulation
3.2 Energy Consumption
3.3 Electricity Cost and Price Signal
3.4 PAR
3.5 System Architecture
3.6 System Model
CHAPTER FOUR
RESULTS/ANALYSIS
4.1 Optimization Technique
4.2 Simulation Results and Discussion
4.2.1 Energy Consumption
4.2.2 Electricity Cost
CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the study
In the current epoch, electricity demand is increasing rapidly. At the same time, electricity generation from different burning fuels is increasing global warming effect; it causes tremendous change in the environment. To handle these problems, consumption of electricity should be minimized, and it can be controlled by optimal consumption of energy. Traditional power grids cannot handle effectively power demand challenges effectively. So, to meet these challenges, a new infrastructure is required. In this regard, smart grid efficiently meets these challenges. SG is the advance form of traditional grid. Smart grid integrates advanced metering infrastructure, control systems, distributed renewable energy generation, bi-directional flow of electricity and communication technologies (Tushar et al., 2015). The two-way information exchange creates an automated energy delivery network. Demand side management (DSM) is an important strategy of smart grid, it maintains balance between load demand and energy supply. To implement effective demand response (DR) strategies, utilities instigate the consumers through incentives, to maintain their load pattern efficiently. Smart meters communicate with control systems and collect information about electricity usage of households. The major objectives of energy planning and control are minimization of electricity cost, curtailment of peak to average ratio (PAR) and maximization of user comfort. We have used metaheuristic firefly algorithm in our HEMS to achieve our objectives. The control parameters of our system are power rating of household appliances, their operational time interval (OTI), length of operational time (LOT) and price signals (Shakeri et al., 2017).1.2 Problem Statement
The optimization techniques for application in electrical system operation and planning are becoming increasingly important, especially with the growth in size and complexity of new mathematical models related to optimization problems of electric power systems. Storage equipment in power system, such as batteries and thermal energy storage (TES), has become increasingly important recently for peak-load shifting in energy systems. Mathematical programming methods, used in the pass to optimize operating schedules, can always be used to derive a theoretically optimal solution, but are computationally time consuming. We use metaheuristics to overcome such limitation.
1.3 Aim and objectives of the study
The main aim of this work is to study the use of metaheuristics for energy system planning and operation.
The objectives of the study are:
- To study how metaheuristics is used in power optimization
- To generate the optimization technique formula for metaheuristics
- To present metaheuristic algorithms
- To provide a comprehensive view on the use of metaheuristics for global optimization problems
1.4 Significance of the study
This study will serve as a means of studying metaheuristics as the fastest means of power system planning and operating optimization technique. The algorithm of this study is used to solve the problem for minimum loss reconfiguration of distribution network.
1.5 Scope and limitation of the study
1.6 Project organization
The rest of the paper is organized as follows: Section 2 presents literature review. Section 3 describes system models and problem formulation. Simulation results and discussion are given in Section 4. This paper is concluded in Section 5.CHAPTER TWO: The chapter one of this work has been displayed above. The complete chapter two of "metaheuristics for energy system planning and operation" is also available. Order full work to download. Chapter two of "metaheuristics for energy system planning and operation" consists of the literature review. In this chapter all the related work on "metaheuristics for energy system planning and operation" was reviewed.
CHAPTER THREE: The complete chapter three of "metaheuristics for energy system planning and operation" is available. Order full work to download. Chapter three of "metaheuristics for energy system planning and operation" consists of the methodology. In this chapter all the method used in carrying out this work was discussed.
CHAPTER FOUR: The complete chapter four of "metaheuristics for energy system planning and operation" is available. Order full work to download. Chapter four of "metaheuristics for energy system planning and operation" consists of all the test conducted during the work and the result gotten after the whole work
CHAPTER FIVE: The complete chapter five of "metaheuristics for energy system planning and operation" is available. Order full work to download. Chapter five of "metaheuristics for energy system planning and operation" consist of conclusion, recommendation and references.
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