Optimisation and Integration of Variable Renewable Energy Sources in Electricity Networks

The growing penetration of renewable energy sources (RESs) into the electricity power grid is profitable from a sustainable point of view and provides economic benefit for long-term operation. Nevertheless, balancing production and consumption is and will always be a crucial requirement for power system operation. However, the trend towards increasing RESs penetration has raised concerns about the stability, reliability and security of future electricity grids. The clearest observation in this regard is the intermittent nature of RESs. Moreover, the location of renewable generation tends to be heavily defined by meteorological and geographical conditions, which makes the generation sites distant from load centres. These facts make the analysis of electricity grid operation under both dynamic and the steady state more difficult, posing challenges in effectively integrating variable RESs into electricity networks. The thesis reports on studies that were conducted to design efficient tools and algorithms for system operators, especially transmission system operators for reliable short-term system operation that accounts for intermittency and security requirements. Initially, the impact of renewable generation on the steady state is studied in the operation stage. Then, based on the first study, more sophisticated modeling on the electricity network are investigated in the third and fourth chapters. Extending the previous studies, the fourth chapter explores the potential of using multiple microgrids to support the main grid’s security control. Finally, the questions regarding the computational efficiency and convergence analysis are addressed in chapter 5 and a DSM model in a real-time pricing environment is introduced. This model presents an alternative way of using flexibility on the demand side to compensate for the uncertainties on the generation side

Optimal Transmission Investment Strategies for Sustainable Power Systems

Maintaining security and reliability in the electricity supply is fundamental to the functioning of a modern society and drives the need for adequate transmission capacity for both market participants and customers. Planning the investment in transmission has always been a complicated undertaking due to the high development costs and long lead times. Furthermore, to anticipate the future needs of customers is a task as difficult as that of cost-effective planning and construction of new facilities. Trying to find treatments for some of these issues represents a major motivation for this thesis. This thesis investigates the problem of how much reinforcement a transmission system requires when a significant proportion of wind generation is integrated into an existing transmission system. A multi-period transmission planning model is developed for determining optimal transmission capacity by balancing amortised transmission investment costs and annual generation costs subject to network security constraints, The model employs the security-constrained DC optimal power flow formulation and applies a solver (DashXpress) to obtain the results of the remaining linear large-scale optimisation problem. This thesis begins by exploring the impact of wind generation on the determination of appropriate levels of system capacity on the transmission network starting from the premise that it is no longer cost effective to invest in sufficient network capacity to accommodate simultaneous peaks from all generators. As such, a significant finding of this study is that conventional and wind generation should share network capacity. Given the acknowledged increase in uncertainty to security of supply due to difficulties in wind generation forecast this thesis also explores the optimal sourcing of generation reserve, and investigates investment in transmission capacity to exploit the cost benefits offered by standing reserve. Finally, the thesis presents and evaluates an alternative associated with transmission operation and investment level of risk and uncertainty by introducing more flexibility to the way the transmission system is operated. Application of Quadrature Boosters and Demand Side as model of corrective control, brings savings in operating costs without jeopardizing the level of system security, enables better utilisation of existing facilities and reduces the demand for new transmission investment

Resilience-Constrained Economic Dispatch for Blackout Prevention

Department of Finance and Education of Guangdong Province 2016[202]: Key Discipline Construction Programme, China, in part by the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group Grant 2016KCXTD022; NSF China under Grant 51707041; State Grid Technology Project Grant 5211011600RJ; Foundation for Distinguished Young Talents in Higher Education of Guangdong under Grant 2016KQNCX045 and 2016KCXTD022)

Bi-Level Optimization Considering Uncertainties of Wind Power and Demand Response

Recently, world-wide power systems have been undergone a paradigm change with increasing penetration of renewable energy. The renewable energy is clean with low operation cost while subject to significant variability and uncertainty. Therefore, integration of renewables presents various challenges in power systems. Meanwhile, to offset the uncertainty from renewables, demand response (DR) has gained considerable research interests because of DR’s flexibility to mitigate the uncertainty from renewables. In this dissertation, various power system problems using bi-level optimization are investigated considering the uncertainties from wind power and demand response. In power system planning, reactive power planning (RPP) under high-penetration wind power is studied in this dissertation. To properly model wind power uncertainty, a multi-scenario framework based on alternating current optimal power flow (ACOPF) considering the voltage stability constraint under the worst wind scenario and transmission N-1 contingency is developed. The objective of RPP in this work is to minimize the VAR investment and the expected generation cost. Benders decomposition is used to solve this model with an upper level problem for VAR allocation optimization and generation cost minimization as a lower problem. Then, several problems related wind power and demand response uncertainties under power market operation are investigated. These include: an efficient and effective method to calculate the LMP intervals under wind uncertainty is proposed; the load serving entities’ strategic bidding through a coupon-based demand response (CBDR) with which a load serving entity (LSE) may participate in the electricity market as strategic bidders by offering CBDR programs to customers; the impact of financial transmission right (FTR) with CBDR programs is also studied from the perspective of LSEs; and the stragegic scheduling of energy storages owned by LSEs considering the impact of charging and discharging on the bus LMP. In these problems, a bi-level optimization framework is presented with various objective functions representing different problems as the upper level problems and the ISO’s economic dispatch (ED) as the lower level problem. The bi-level model is addressed with mathematic program with equilibrium constraints (MPEC) model and mixed-integer linear programming (MILP), which can be easily solved with the available optimization software tool

A review of co-optimization approaches for operational and planning problems in the energy sector

This paper contributes to a comprehensive perspective on the application of co-optimization in the energy sector – tracking the frontiers and trends in the field and identifying possible research gaps – based on a systematic literature review of 211 related studies. The use of co-optimization is addressed from a variety of perspectives by splitting the studies into ten key categories. Research has consistently shown that co-optimization approaches can be technically challenging and it is usually a data-intensive procedure. Overall, a set of techniques such as relaxation, decomposition and linear approaches have been proposed for reducing the inherent nonlinear model's complexities. The need to coordinate the necessary data from multiples actors might increase the complexity of the problem since security and confidentiality issues would also be put on the table. The evidence from our review seems to suggest a pertinent role for addressing real-case systems in future models instead of using theoretical test cases as considered by most studies. The identified challenges for future co-optimization models include (i) dealing with the treatment of uncertainties and (ii) take into account the trade-offs among modelling fidelity, spatial granularity and geographical coverage. Although there is also a growing body of literature that recognizes the importance of co-optimization focused on integrating supply and demand-side options, there has been little work in the development of co-optimization models for long-term decision-making, intending to recognize the impact of short-term variability of both demand and RES supply and well suited to systems with a high share of RES and under different demand flexibility conditions. The research results represent a further step towards the importance of developing more comprehensive approaches for integrating short-term constraints in future co-optimized planning models. The findings provide a solid evidence base for the multi-dimensionality of the co-optimization problems and contriThis work is supported by the National Council for Scientific and Technological Development (CNPq), Brazil. This work has been supported by FCT – Fundaça˜o para a Ciˆencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020