Optimal energy control of a grid connected solar-wind based electric power plant.

PLAGIARISM

PUBLICATIONS

I would like to express my sincere thanks to the University of KwaZulu-Natal for financial assistance. The results demonstrated in the thesis prove the merits of the approach and its robustness to uncertainties and external perturbations. P : Control variable representing the energy flow from the PV generator to the main bus at any hour (kW).

INTRODUCTION

RESEARCH BACKGROUND AND JUSTIFICATION
PROBLEM STATEMENT

Sub-objective 1
Sub-objective 2
Sub-objective 3
Sub-objective 4
Sub-objective 5

METHODOLOGY
RESEARCH DELIMITATIONS
BENEFITS OF THE PROJECT
ORGANISATION OF DISSERTATION

The objective function is multi-objective, where the first term of the objective function is related to the DG fuel cost. To improve the system with the renewable energy as a source, the optimization and the control of the energy are used by a control variable by connecting a hybrid system to the national grid. Using the Matlab environment, determining the optimal model and maximizing the sale of energy to the network; in the first model, the DG fuel costs of the hybrid power plant will be minimized.

LITERATURE REVIEW

INTRODUCTION
OBJECTIVE FUNCTION
OPTIMISATION METHODS APPLIED TO RENEWABLE ENERGY
WIND POWER
SOLAR ENERGY
HYDRO POWER
HYBRID SYSTEM
SUMMARY

In [47], the authors compare the competitiveness of renewable energy and the different places where it can be used. There has been great interest in the optimal dimensioning of the design for renewable energy-based greenhouses. In [146], the authors presented an evolution model of the capacity of a mini-hydro plant based on a forecast series model.

OPTIMAL ENERGY CONTROL OF A GRID-CONNECTED SOLAR-WIND-

INTRODUCTION
PROPOSED TECHNIQUES
PHOTOVOLTAIC MODEL
WIND MODEL
DIESEL GENERATOR MODEL
BATTERY MODEL
MODEL DESCRIPTION
PROPOSED MODEL

Objective Function
Data Presentation
Power Sources
Grid Power and Local Load

RESULTS AND DISCUSSION
SUMMARY

This statement is expressed by equation (3.3) to ensure that any hybrid power sold to the grid must first meet the requirements of the local load, which implies that any subsource can supply the local load and the grid. IPV is the solar radiation incident on the PV array, which is expressed in [kWh/m2] and  is the efficiency of the PV generator. Thus, the operation of the DG in the system promises to be more economical in terms of DG energy, and the generator is dispatched only when there is a need to meet the local load demand.

If the PV and PW generators cannot meet the local load requirement, the battery will be discharged from its maximum value until one of the PV or PW sources is able to supply again. The network is delivered after the local requirements are met at different times of the day and in accordance with the control. Equations (3.17) and (3.19) were satisfied, which meant that the output of the renewable energy sources had to be equal to or greater than the load to satisfy the local load or sale to the grid during peak times and to charge the battery.

During the early morning hours, the load is usually supplied by the DG, PV, PW and the battery. The DG operation and the amount of power supplied by the DG relies on the SOC of the battery and the amount of power flowing from the renewable energy sources. Tables 3-1 to 3-4 show the energy sold to the grid at different times of the day.

These results further show that seasons are an important element to take into account as they influence DG use and energy flow.

OPTIMAL ENERGY CONTROL OF A GRID-CONNECTED SOLAR-WIND

INTRODUCTION
PRINCIPLE OF OPERATION
DESIGN MODEL

Pump hydro storage
Optimisation

APPLICATION OF THE MODEL

Model parameters
Power sources
Grid and locl load profile

RESULTS AND DISCUSSION
SUMMARY

The second objective is to minimize the energy supplied by the main bus to the pump (P3) and is expressed by:. Equation (4.11) implies that the power delivered by the PV, PW and the hydropump is equal to the total power delivered and Kirchhoff's law, which is applied to determine the total power in the main bus. Equation (4.12) ensures that the power supplied by the PV, PW and the hydro pump at any hour of the day is equal to that of the local load and the load to be supplied to the network during the peak time.

P1(k), P2(k) and P4(k) are the control variables representing the power flow from PV, PW and the power supplied by the PH system to the load at time (t), while P3(k ), P5( k) and (P6) are variables that represent the flow of energy to the pump, the flow of energy to the local load and the flow of energy to the network. Energy is supplied to the grid to increase sales when local demands in the control horizon are met at different times of the day. At night and early in the morning, the load is covered by the PHS system, and sales to the grid are highest during peak periods.

Constraints (4.16) and (4.17) are satisfied by ensuring that the power delivered by PV, PW and the hydropump system at any time of the day corresponds to the local load and the load delivered to the grid during the peak period ; it must thus be equal to or greater than the total loads. At that point, the turbine shuts down until the PV and PW are unable to produce enough energy to meet the local load and the load to the grid. The higher power delivered during the low demand season is attributed to higher PV output to the local load and low energy consumption.

The optimization model developed in this chapter aimed to achieve the maximization of energy sales in the network.

Figure 4-2: Low demand season – weekday power flow

A MODEL PREDICTIVE CONTROL STRATEGY FOR GRID-CONNECTED

INTRODUCTION
PRINCIPLE OF OPERATION
SUB-MODELS
OPEN-LOOP CONTROL
MODEL PREDICTIVE CONTROL CONFIGURATION

Discrete linear model of the MPC
Constraints
Implementation of the main algorithm

MODEL PARAMETERS

Power sources
Grid power and local load

RESULTS
SUMMARY

Equation (5.5) ensures that the power supplied by the PV, PW and the hydro pump at any hour of the day is greater than or equal to the local load and the load to be supplied to the grid during the peak period. Model predictive control can be defined as closed-loop control of the installation, the purpose of which is to predict the output. The performance index or objective function of the MPC is described by equation (5.13).

The MPC application model is given by equations (5.9) and (5.10), model inputs are defined by energy flow from PVp1(k) , energy flow from PW p2 k , energy input s p3 k , energy, supplied by turbine p4(k), PL(k ) is defined as the load demand at the kth sampling time, and the outputs can be defined by. At night and in the early hours of the morning, the load is covered by the PW and PH system. It is obvious that it is possible to plan the use of different energy sources in a closed-loop system to meet the needs of loads and sell energy in the system.

In the case of PV and PW, there is enough energy to supply the local load; so the generator (P4) can meet the load demand. A comparison of the performance of the open-loop control system and the closed-loop control system shows that they are similar when there are no perturbations in the response of the optimization model, as is evident in Figures (5-3) and (5 ). -5). When we compare the performance of open-loop control systems and closed-loop control systems with the disturbances shown in Figures (5-7) and (5-9), it is evident that the performance of the closed-loop system is more well regarding to concerns.

A comparison of the performance of open-loop control and closed-loop systems with and without disturbances during a 24-hour period was used as a control horizon.

OPTIMISATION AND SALE OF ENERGY TO THE GRID WITH SPECIAL

INTRODUCTION
MODEL OF NETWORK DYNAMICS
NETWORK UNIT MODEL
OPTIMISATION MODEL

Objective function
Constraints
Application of the Model

MODEL PARAMETERS

Power sources
Grid power and local load

RESULTS
SUMMARY

Local loads are represented by the clinic and houses and the network, which is the connection between the network and the renewable source. Maximizing energy sales is achieved by selling more energy to the national grid during peak periods when energy costs are higher than during off-peak and standard periods. The electric torque (Te) of each turbine unit will also increase, providing an increase in load; at the same time, the mechanical torque (Tm) of the turbine initially remains constant. 6.5) The following general expression derived from equation (6.5) applies to the change in rotor speed, which means that a change in network load will be reflected in a change in speed or frequency.

At the time of the increase in frequency in the grid, the renewable energy sources reduce their output by a margin compared to the power output immediately before the grid frequency increase. The second step of the constraint is linked to the speed or the frequency, which means that the constraints represented in equations (6.13 to 6.19) are represented in this model as equation (6.21), in the case of the frequency. If there is an increase or decrease in the load in the network, there will be a change in the frequency or the speed of the rotor in the conventional power source.

Constraints (6.19) and (6.13) are satisfied by ensuring that the power supplied by the PV, PW and HP system at any time of the day corresponds to the local load and the load supplied to the grid during the peak period; it must thus be equal to or greater than the total loads. The reservoir dynamics are shown in Figures (6-2) and (6-4), and the network dynamics are represented by Figures (6-3) and (6-6); it can be seen on the frequency at the time the rotor speed changes. In this case, the conventional source has a reserve margin of less than 8%, it can be seen through Figure 6.8 the difference between the power supply from the grid and the load demand.

The results show minimization of pump consumption and maximization of energy sales.

Figure 6-1: Low demand season – weekday power flow

CONCLUSION AND FUTURE RESEARCH

CONCLUSION

FUTURE RESEARCH

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New approach for optimizing the placement of wind turbines within a farm using genetic algorithms.” Renewable energy. Dynamic modeling and sizing optimization of stand-alone photovoltaic energy systems using hybrid energy storage technology.” Renewable energy. Impact of the load profile on the gross energy demand of stand-alone photovoltaic systems.” Renewable energy.

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Simulation and optimization of the size of a pumped storage power plant for the recovery of rejected energy from wind farms.” Renewable energy.