Unit Commitment In Energy System Having Renewable Sources

Unit commitment in energy systems with renewable sources refers to the optimization process of scheduling power generation units to meet electricity demand while considering the intermittent nature of renewable energy resources such as wind and solar. This involves determining the most cost-effective combination of conventional and renewable energy sources to satisfy demand, minimize operating costs, and ensure system reliability. The challenge lies in integrating fluctuating renewable energy outputs with dispatchable power sources like natural gas or hydroelectric plants, while also accounting for factors like weather forecasts, grid constraints, and storage capacity. Unit commitment algorithms aim to balance the intermittency of renewables with the need for stable power supply, optimizing generation schedules over short and long time horizons to enhance efficiency and sustainability in the energy grid.

ABSTRACT

This paper presents a real approach for thermal unit commitment (UC) problem solution in energy system. The proposed methodology consists of four conventional thermal generating units integrated to the inclusion of Photovoltaic (PV) power, Wind Turbine Generators (WTGs), and Battery Energy Storage System (BESS). Minimization of the total daily operating cost is considered as the objective function in two cases. In the first case, UC with thermal units considering the IMP, PV and BESS is described. In the second case, WTGs are introduced beside high penetration of PV and BESS and the IMP is removed in order to get rid of its economical and mostly political problems. MILP (Mixed-Integer Linear Programming) is used here as the optimization technique to obtain an optimal unit commitment problem solution with consideration of PV, WTGs and BESS. The effectiveness and robustness of the proposed scheme is verified by numerical simulations using MATLAB environment.

 

 

TABLE OF CONTENTS

 TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

TABLE OF CONTENT

ABBREVIATION AND THEIR MEANING

CHAPTER ONE

  • INTRODUCTION
  • BACKGROUND OF THE STUDY
  • PROBLEM STATEMENT
  • AIM/OBJECTIVE OF THE STUDY
  • SIGNIFICANCE OF THE STUDY
  • SCOPE OF THE STUDY

CHAPTER TWO

LITERATURE REVIEW

2.0                                                    LITERATURE REVIEW
2.1      REVIEW OF THE STUDY
2.2     DESCRIPTION OF UNIT COMMITMENT
2.3     REVIEW OF THE RELATED STUDY
2.4     RECENT TRENDS IN UC

CHAPTER THREE

3.0      METHODOLOGY

3.1      PROPOSED POWER SYSTEM DESCRIPTION

3.2      SYSTEM MATHEMATICAL MODELING

3.3      SYSTEM MODELING

3.4      FORMULATION

CHAPTER FOUR

4.0       SIMULATION RESULTS AND DISCUSSIONS

CHAPTER FIVE

  • CONCLUSIONS

REFERENCES

 

AREVIATIONS AND THEIR MEANING

UC              Unit Commitment

PV               Photo-Voltaic

WG             Wind Generation

BESS           Battery Energy Storage System

WTGs         Wind Turbine Generators

TGs             Thermal Generators

IMP            Imported Power

RESs           Renewable Energy Sources

SOC            State of Charge

DOD           Depth of Discharge

TDOC         Total Daily Operating Cost

TG               Thermal Generator

SUC            Start Up Cost

NTG            Number of Thermal Generators

NPV            Number of PV panels

NWTG       Number of Wind Turbine Generators

NBESS        Number of Battery Energy Storage System

MILP          Mixed-Integer Linear Programming

 

 

CHAPTER ONE

1.0                                                        INTRODUCTION

1.1                                          BACKGROUND OF THE STUDY

Power sector of Nigeria is one of the major challenged sectors that have hindered the country from being developed (Salifou, 2015). Electricity is an important step toward enhancing people’s opportunities and choices. Access is key to boosting economic activity and contributes to improving human capital, which, in turn, is an investment in a country’s potential (Salifou, 2015).

In Nigeria, the electrical energy supplied to load demand is being generated by hydro-plant or thermal generating units, and the cost of generating this power is too high because of transportation cost, storage cost, and so on. However, power producers should use an optimal operation of thermal units for high efficiency, operational cost and carbon emission reductions. To achieve the above-mentioned points, UC must be incorporated into the power system. UC is an operational planning. The purpose of this planning is to determine a schedule called Unit Commitment Schedule which tells us beforehand when and which units to start and shut down during the operation over a  pre-specified time, such that the total operating cost for that period becomes minimum (Ravi, 2017). The objective of the UC is to obtain the minimum cost without violating the system constraints, this cost includes the generators start-up cost (SUC), fuel cost and shut down cost (Sarjiya, 2015). Unit commitment of thermal power plant in integration with wind and solar plant using genetic algorithm is described here.

In addition, the use of conventional energy sources like coal, gas, oil through the burning process to generate electrical energy leads to the release of the greenhouse gases which in turn leads to global warming. To save the earth and future generations, power producers are accommodating recently the use of non-conventional energy sources like solar energy, wind energy and several other forms of naturally available energies. It is better to integrate them with the conventional energy sources to solve problems such as the mismatch between power generation and load demand, to secure the minimum total cost of the system, the global warning eradication, etc.

However, it is difficult to predict the power generated by the renewable resources such as PV and WG because they have certain intermittent and uncertainty. To suppress their intermittent, compensating their fluctuations by other generators is an efficient method to ensure the system stability and economy. Their inclusion into the power system brought major challenges as a result of their output variations. As countermeasures, the Battery Energy Storage System (BESS) is introduced into the power system under study. BESS is an effective system that can be used to supplement the power output variation of the renewable sources. It is required to solve the optimal capacity because it is costly (Kazuki et al., 2013). BESS is developed to achieve longer usage time, and better efficiency, which are very useful in electricity business. In fact, the introduced BESS increases the capital cost as well as overheads resulting from the maintenance due to the characteristic deterioration of the battery.

In this paper, an optimization approach is proposed to determine an optimal thermal unit commitment problem solution with the inclusion of PV, WTGs and BESS. Minimization of the total daily operating cost is considered here as the objective function. The proposed method uses MILP as optimization method to tune the values of decision variables in two cases as follows: In the first case, PV and BESS are integrated with the conventional power plant (four TGs and IMP) while as in the second case, IMP is discarded through more penetration of PV and WTGs to the four TGs. Also, BESS is considered. The effectiveness of the proposed method is confirmed by simulation results on MATLAB

1.2        PROBLEM STATEMENT

UC is considered to be an important task in power system operations. Searches for the maximum cost-effective generators commitment decision of the power system to satisfy the load demand while meeting all the operational constraints on the generation resources and transmission system. This is considered a challenging problem on account of a high level of uncertainty in the load which results from the uncertainty of renewable generation.

The high penetration of renewable energy increases the instability of the network since the renewable energy system depends on the state of nature. The uncertainties of the operation of a power system integrated with renewable energy mainly include the uncertainty of renewable energy generation related to forecasting error. To reduce the danger of the uncertainty of renewable energy, many possible scenarios have to be studied for short-term and long-term planning. This can be done by generating more than one scenario for wind and solar performance by using reliable forecasting approaches to reduce the level of error in planning systems. Then by UC, the best operation schedule can be determined.

1.3                  AIM AND OBJECTIVES OF THE STUDY

The main aim of this study is the application of unit commitment in energy system with renewable energy sources – wind, solar and battery storage. The objectives of the study are:

  • To plan the day-ahead performance of generating units in an electrical network by using the UC
  • To forecast the day-ahead performance of a wind

 

  • To generate more than one wind power scenario for a day ahead
  • to solve problems such as the mismatch between power generation and load demand
  • to secure the minimum total cost of the system, the global warning eradication
  • To make comparisons between GA and DP methods in solving the UC

1.4                                       SIGNIFICANCE OF THE STUDY

This study will serve as a means of exposing the knowledge of the reader to renewable energy sources such as wind and solar energy thereby saving the earth and future generations. The study will serve as a means of teaching the student involved about renewable energy integration with power system.  Finally, the study will help in solving problems such as the mismatch between power generation, load demand to secure the minimum total cost of the system, the global warning eradication.

1.5                                              SCOPE OF THE STUDY

The scope of this study involves using an optimization approach to determine an optimal thermal unit commitment problem solution with the inclusion of PV, WTGs and BESS. Minimization of the total daily operating cost is considered here as the objective function.

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