Electric Power Generation And Transmission For Expansion Planning Using Econometric Model

ACRONYM AND THEIR MEANING

SA– simulated annealing

GA – genetic algorithm

GTEP -Generation and transmission expansion planning

TLBO – Teaching Learning Based Optimization

DRPs – Demand response programs

MGTEP – model for transmission and generation expansion planning

PV – photovoltaics

FACTS -Flexible Alternating Current Transmission System

ISO – Independent System Operator

DR – demand response

ABSTRACT

Generation and Transmission Expansion Planning (GTEP) involve determining if and how generation and transmission lines should be added to the power grid so that the operational and investment costs are minimized. GTEP is a major issue in smart grid development, where demand response resources affect short- and long-term power system decisions, and these in turn, affect GTEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in GTEP. Then, the GTEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load

ABSTRACT

TABLE OF CONTENT

LIST OF FIGURES

LIST OF TABLES

CHAPTER ONE

INTRODUCTION

1.1     BACKGROUND OF STUDY

1.2     PROBLEM STATEMENT

1.3     AIM OF THE PROJECT

1.4     OBJECTIVE OF THE PROJECT

1.5     SCOPE OF THE PROJECT

1.6     SIGNIFICANCE OF THE PROJECT

CHAPTER 2: LITERATURE REVIEW

2.1     INTRODUCTION

2.2     REVIEW OF THE STUDY

2.3     REVIEW OF THE RELATED STUDY

2.4     MODELS FOR GENERATION AND TRANSMISSION EXPANSION

2.5     EXPANSION PLANNING PROBLEM

CHAPTER THREE:

METHODOLOGY

3.1     INTRODUCTION

3.2     SYSTEM FLOWCHART

3.3     TLBO ALGORITHM

3.4     GENERATION AND TRANSMISSION EXPANSION PLANNING MODEL

CHAPTER 4: RESULTS AND DISCUSSION

4.1     STUDY OF THE NETWORK, SIMULATION RESULTS AND DISCUSSION

CHAPTER 5

 CONCLUSION AND RECOMMENDATIONS

5.1     CONCLUSION

5.2     RECOMMENDATION

5.3     REFERENCE

CHAPTER ONE

                                                                           1.0   INTRODUCTION

Installing new devices on an existing power system while ensuring stability and reliability of the power system are the main goals of Generation and Transmission Expansion Planning (GTEP). This planning is based on load prediction and power supply conditions. From a mathematical view point, GTEP is a nonlinear, discrete, and large-scale optimization problem with many equality and inequality constraints. Transmission line planning can be divided into evolutionary, mathematical, and meta-heuristic methods. The evolutionary method quickly converges to the optimal solution, but for a large scale and complex problem, it can converge to a solution that is far from ideal. One of the first methods for solving the expansion transmission network problem was presented in 1970 by Garver [1]. In this work, the problem is formulated as a load distribution problem; the objective function and the constraints are described by linear functions that neglect Ohmic power loss. Considering the newly added lines, new linear load flow is calculated, and the operation continues until no overload exists in the system. Lattore et al. proposed an evolutionary method in which the transmission line is decomposed into two problems: generation and investment [2]. The investment problem is solved by an evolutionary method, while the generation problem is solved by a known optimization method.

1.1                                           BACKGROUND OF THE STUDY

The function of an electric power system is to provide a reliable and continuous source of electricity whenever requested. To provide this service, each of the three main components of an electric power system — generation, transmission and distribution — must perform as required. The generation system consists of physical facilities that convert energy resources (e.g. coal, oil, uranium, running bodies of water) into electricity. The transmission system then transports the generated electricity to the local service communities. The distribution system within each community provides the actual connection from the transmission system to each customer, and enables the customer to consume electricity upon demand. An electric power system is a dynamic system which is a balance of supply and demand:(a) The supply of electricity, consisting of physical devices that must be designed, constructed, operated, maintained, and eventually replaced as each device wears out, and(b) The demand for electricity, which changes as a function of time from instantaneous (seconds, minutes), to short term (hours, days) and to the longer term (months, years).Therefore, a major objective for an electric power system is to keep a continual balance between the supply and demand for electricity. Power system expansion planning is the process of analysing, evaluating and recommending what new facilities and equipment must be added to the power system in order to replace worn-out facilities and equipment and to meet changing demand for electricity. Planning methodologies have been developed for the three main components of a power system (generation, transmission, distribution), and each one is in itself a major subject of study.

1.2                                                  PROBLEM STATEMENT

In the last decade, the question of future energy supplies has become one of the central political challenges in almost all countries of the world. Since the oil crisis in 1913 energy problems have moved to the core of the most difficult and controversial issues confronting society.

The radically increased public awareness of the energy problem has initiated a remarkably large number of energy policy studies and has given a substantial impetus to the development of energy models to help decision-makers deal with the broad variety of issues related to the energy problem. A large number of energy models have been developed all over the world and are now used for energy and policy planning purposes on a regional, national as well as on an international scale. The scope of energy models ranges from engineering models of different energy conversion technologies (e.g. refineries), sectoral models dealing with the demand and/ or supply of single fuels, energy system models encompassing the entire energy system to models describing the energy system as an integral part of the overall economy.

This survey is not intended to give an exhaustive description of the energy modes developed so far, or to evaluate the different methodologies applied in energy models. Rather, a limited number of representative models are described to illustrate the present state of the art. I Therefore, we will concentrate on energy system and energy economy models for strategic planning and policy analysis. Before discussing specific models in some detail, we will give a brief overview of the history and methods used in energy modeling and we will outline the nature of the issues facing the energy planner and energy policy-maker, which are characterized by complexity and uncertainty. The paper concludes with a discussion on unresolved modeling issues and some recommendations on how to improve the usefulness and impact of energy models in energy expansion planning

1.3                                                   AIM OF THE PROJECT

This work presents an overview of selected existing power system distribution expansion models. This work discusses the effects of demand response programs on reducing the final costs of a system in GTEP. Then, the GTEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs.

1.4                                             OBJECTIVE OF THE PROJECT

The wider objectives of the energy plan are less easy to categorize than the basic goals. They are much more diverse and are sensitive to the needs of a particular country. It is, however, important for energy planners to attempt to arrive at a statement of objectives before proceeding into extensive analysis. Reaching such a consensus will reduce inefficiency, focus attention on the key issues, and help organize the efforts of the participants in the planning process. The following examples illustrate the statement of wider objectives of the energy plan:

1. To develop the energy supply system leading to lowest cost to consumers.
2. To maximize reliability and safety in the energy supply system.
3. To develop a diversified energy supply system with less dependence on imported oil.
4. To maximize the use of indigenous energy supplies
5. To maximize the use of renewable resources
6. To provide energy for optimum industrial development
7. To reduce the use of non-commercial fuel and subsequent deforestation
8. To minimize environmental effects.

1.5                                                      SCOPE OF THE PROJECT

This work is concerned with the methodologies developed for planning the expansion of the generation and transmission component of a power system. Since all three components are interrelated, and each can affect the planning of the other two, any expansion plan developed for one component should be evaluated taking the others into account. Therefore, even though this work discusses planning the generation component of an electric system, the plan should also be evaluated in terms of the transmission and distribution at some point in the planning process.

1.6                                          SIGNIFICANCE OF THE PROJECT

This study is important in that it help us to determine the optimal pattern of system expansion to meet the electric energy requirements of a country over a given period. And the model used is the one less cost model. The generation and transmission expansion planning (GTEP) is also useful in determining the lowest cost of investment for new transmission assets that must be installed in a power system to attend a forecasted demand within a given time horizon [1]. The fact that TNEP has a long-lasting impact on systems operation makes it one of the main strategic decisions in power systems.