Advanced design methods for active distribution networks

Abstract

Growing concerns about climate change and desire to reduce CO2 emissions lead to the wide spread of distributed generation (DG) units based on renewable energy sources (RES) and electrification of the heat and transportation sectors. However, such measures result in the challenges experienced by the transmission system operator (TSO) and distribution system operator (DSO) in their networks. Negative generation prices, power congestions and the problem with voltages can be caused due to the adoption of environmentally-driven policies. Conventional solutions treating the networks as passive distribution networks (PDNs) will require large capital expenditures (CAPEX) into the network reinforcement. An alternative solution is to apply the approaches from the active distribution network (ADN) planning and use the flexibility services (FSs) from the active elements (AEs), that could be found at the distribution level. Customers participating in demand response (DR) programs, battery energy storage system (BESS), circuit breakers (CBs) used in the reconfiguration (RE) and underground cables providing dynamic line rating (DLR) are all examples of such AEs. This PhD thesis is focusing on the problem of integration of AEs into ADN planning. In order to achieve such integration, first, the influence of the inclusion of the FSs on the whole planning process should be determined. Second, different types of AEs providing FSs have to be compared with each other and conventional solutions on a common basis. A generic planning framework is proposed in order to streamline the planning process. The framework shows that ADN planning is a complex process consisting of multiple stages. Each stage includes different planning algorithms that focus on individual planning aspects (forecasting, verification, etc.). The framework could be used for the development of the various planning procedures, that combine different algorithms and allow to integrate new emerging technologies into ADN planning. The integration of AEs will evoke changes on each of the planning stages, such as the need for time-series power demand data and the ability to relax the design criteria applied to the network design (ND) part of the planning solution. Multiple AEs that can provide FSs useful for the DSO can be present in the network. The second methodology - flexibility characterization framework is proposed to enable the comparison between different AEs via cost. The framework allows to characterize the cost of any AE in a generic manner regardless of the AE’s type and technology, using the combination of different parameters and cost functions to estimate its CAPEX and operational expenditures (OPEX). The estimated cost is then compared with the Value of Flexibility (VoF), which determines the maximum price DSO is ready to pay for the provision of FSs. VoF is estimated by analyzing conventional planning solutions (without application of FSs). The flexibility characterization framework serves as a decision support tool for the DSOs willing to implement FSs from AEs in planning. Case studies based on the real distribution network of Nordhavn in Copenhagen, Denmark show the validation of the proposed methodologies. Case studies 1 and 2 present the examples of integration of AEs in the ADN and integrated energy system (IES) planning, case study 2 shows how to compare FSs from DR, BESS, RE and DLR and select the best option for solving a power congestion. Case studies show that the application of the proposed methodologies can allow the easier integration of AEs, which results in a more cost-efficient solution.