AbstractThe increasing penetration of low carbon technologies (LCTs) at customers’ premises, such as schools, homes and data centres, presents new opportunities for customers to take an active part in reducing energy and network costs through Demand Side Response (DSR). Meanwhile, the in depth DSR benefits on downstream network architecture, e.g. small and medium demand customers and distribution network operators, could be fully explored. Turning LCT into useful DSR resources to reduce energy volume or shift energy over time requires sophisticated control that can balance interests between customer, network and whole-sale energy market. The limitations of the current DSR control approaches are: 1) complex or inaccurate to formulate the increasingly complicated power flow brought by LCTs; 2) lack of interest balance between customers and network operators; 3) not able to facilitate customers in accessing to both local and central energy market. This research proposes a range of optimal DSR models in the low carbon environment to introduce three key innovations to overcome the limitation:1) a new problem formulation in DSR optimization model to maximize the customers’ DSR return. The proposed formulation generalizes the relationship between power and final energy cost as the simple piecewise functions. The enhanced formulation reduces optimization problem solving complexity and extends modelling capability for conversion efficiency in both local AC and DC low carbon network.2) a new Mixed Integer Linear Programming (MILP) based DSR optimization model that integrates the network demand reduction signal into the constraints of problem formulation to improve network operators’ benefit. This research also proposes a novel probability-based quantification method to assess the minimum DSR penetration for concrete network demand reduction considering the demand uncertainty.3) a new MILP based DSR trading model in the market environment of both local and central energy markets. Given different price signals, the proposed model determines the most profitable DSR trading behaviours for DSR providers across central and local energy markets.
|Date of Award||1 Nov 2017|
|Supervisor||Furong Li (Supervisor)|
Multi-Value Demand Side Response for Low Carbon Networks
Zhao, C. (Author). 1 Nov 2017
Student thesis: Doctoral Thesis › PhD