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DR policy framework
For coordinated and optimal operation of any DR program, a robust and coherent policy framework is required. The purpose of this framework is to manage all assets and acquired services and its associated policy instruments. The framework set the direction for the entire management of the DR program to ensure that the performance of these activities offer value for money and demonstrates sound stewardship in the delivery of the program. The consequence of failure to properly manage and control these activities can result in increased program and administrative costs, and the likelihood of compromising the outcomes of the program. In addition, the framework pinpoints key policy instruments, professional ethics, legislation, standards and requirements for integrated information systems that comprise the basis for the management practices and controls.
General modelling of RDR
The electricity consumption in a household primarily depends on the power consumption of the electrical appliances and the behaviour of the occupants using them [54–56]. Two approaches to residential load profile modelling are presented by the literature: top-down and bottom-up approaches [57–61]. The granularity of data in these two methods differs; the former offers aggregated data whereas the latter offers fine and more informative data in the area of household energy management. The top-down model is not concerned with individual end-uses and their activities or behaviour [57, 62, 63]. Although top-down models rely on readily available historic, aggregated energy data, which makes them straight-forward to implement, this approach however, makes it harder to model changes in energy consumption because of lack of data on how energy is consumed, therefore it makes it difficult to recommend changes related to behaviour. In the bottom-up model, the load is constructed from households’ individual appliances. This model has been shown to offer high resolution to load modeling [58–61]. In this thesis, formulation of optimization model for residential energy consumption under demand response is carried out using a bottom-up approach through individual appliance scheduling. It has been stated in [61] that in residential energy modelling, the bottom-up method promotes efficiency.
Developing applicable models of residential loads for smart grid applications is a critical issue to allow practical models of electrical energy usage patterns. Due to the complex interactions between residential customers and the power utility companies, DR has often been studied using various techniques from game theory, optimization, and microeconomics. The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. In this work, residential DR is studied using the optimization approach. Non optimal control modelling methods have been proposed in the literature with or without consideration of the consumer’s behavior. In office occupant electricity consumption behaviour is studied through a developed data mining approach. The following literature review subsection focuses on the optimization mathematical modelling method adopted in this thesis. The advantage of this method is that it offers optimum solution.
Storage system
Battery energy storage systems (BESS) are an option to provide peak shaving and valley filling of the residential load profile [86,87]. Electric vehicles and conventional batteries have over the years been used as residential energy storage devices [88]. There are two main applications of BESS in the residential sector. Firstly there is the off-grid hybrid energy solution, where two or more different types of renewable energy sources are integrated together with a storage device. This method is mostly applied to rural settlements where there is no access to the grid power [89–91]. This has been extensively studied in literature [90, 91].
The second application of BESS is a backup system for a household connected to the grid19. This application is motivated by unreliable and intermittent electrical power supply. In this application the BESS is connected to the grid. It can be available as a compact backup electronic power supply system, interruptible power supply (UPS). Usually in an African setup, buying a UPS is not affordable to many because of its high cost due to its technological enhancement features such as longer life and less maintenance. The ideal solution is to build the system. These systems can be designed in any size, based on the application. In [92], a detailed design of flat plate lead acid batteries for the study of power flow management for grid interconnection of PV and batteries has been carried out. In these references [92, 93]; a detailed model of the battery as a storage system connected to PV system under given regulatory conditions is presented, also battery sizing is performed [93]. Although these provide in-depth study of the battery, our work however considers elementary usage of the device. The following subsection gives a brief literature review on PV systems.
PV system
The use of renewable energy sources (RES) has become inevitable in today’s electrical energy system because of their sustainability and their environmental advantage. In smart grid applications, use of renewable energy sources at residential level cannot be ignored as many countries including South Africa, have rolled out such systems mainly through household roof-top connections. South Africa has over the years implemented residential rooftop PV systems; however grid connection of small-scale renewable electricity generation is yet to be implemented because South Africa’s national energy regulator (NERSA) is currently in the process of developing the regulatory framework on small-scale renewable embedded generation sources and the guidelines on electricity reseller tariffs27. Some of the challenges with small-scale renewable generation grid tie include but are not limited to reverse power flows and metering tariff solutions. For this reason, in this work, we consider households with dedicated solar PV and storage systems, without infeed to the grid. Therefore the purpose of the PV is to charge the battery, which will in turn discharge during peak times to relieve the grid. In [85], the thesis evaluates two formulations to schedule smart home appliances with respect to economic benefits and environmental benefits.
CHAPTER 1 Introduction
1.1 Problem statement and motivation
1.2 Research objectives and scope
1.3 Research contributions
1.4 Thesis layout
CHAPTER 2 Literature review and background study
2.1 Chapter overview
2.2 Demand response
2.3 Storage and renewable energy sources
2.4 Chapter summary
CHAPTER 3 Optimal scheduling of household appliances for demand response
3.1 Chapter overview
3.2 Problem definition and model formulation
3.3 Case study
3.4 Simulation results and discussion
3.4.1 Further discussions
3.5 Chapter summary and conclusion
CHAPTER 4 Optimal scheduling of household appliances with battery storage and coordination
4.1 Chapter overview
4.2 Problem definition
4.3 Mathematical model formulations
4.4 Model parameters
4.5 Solution methodology
4.6 Measured, simulation results and discussions
4.7 Chapter summary and conclusion
CHAPTER 5 Combined residential demand side management strategies with coordination and economic analysis
5.1 Chapter overview
5.2 Problem definition
5.3 Optimization model
5.4 Data
5.5 Solution methodology
5.6 Simulation results and discussion
5.7 Economic analysis
5.8 Chapter summary and conclusion
CHAPTER 6 Summary, conclusions and future work
6.1 Summary
6.2 Conclusions
6.3 Future work
