Metaheuristics For Energy System Planning And Operation

ABSTRACT

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

1. CERTIFICATION

III. DEDICATION

1. ACKNOWLEDGEMENT
2. 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:

1. To study how metaheuristics is used in power optimization
2. To generate the optimization technique formula for metaheuristics

• To present metaheuristic algorithms

1. 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

There are different types of optimization techniques used in power system planning and operation. However, this study covers only the use of metaheuristics  which is an approximate method of energy system optimization techniques.

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.