Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> This paper presents the application of information gap decision theory (IGDT) to help the distribution network operators (DNOs) in choosing the supplying resources for meeting the demand of their customers. The three main energy resources are pool market, distributed generations (DGs), and the bilateral contracts. In deregulated environment, the DNO is faced with many uncertainties associated to the mentioned resources which may not have enough information about their nature and behaviors. In such cases, the classical methods like probabilistic methods or fuzzy methods are not applicable for uncertainty modeling because they need some information about the uncertainty behaviors like probability distribution function (PDF) or their membership functions. In this paper, a decision making framework is proposed based on IGDT model to solve this problem. The uncertain parameters considered here, are as follows: price of electricity in pool market and demand of each bus. The robust strategy of DNO is determined to hedge him against the risk of increasing the total cost beyond what it is willing to pay. The effectiveness of the proposed tool is assessed and demonstrated by applying it on a large distribution network. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> A method for solving the optimal power flow (OPF) problem including HVDC connected offshore wind farms is presented in this paper. Different factors have been considered in the proposed method namely, voltage source converter (VSC-HVDC) and line-commutated converter high-voltage DC (LCC-HVDC) link constraints, doubly fed induction generators' (DFIGs) capability curve as well as the uncertainties of wind power generation. Information gap decision theory (IGDT) is utilized for handling the uncertainties associated with the volatility of wind power generation. It is computationally efficient and does not require the probability density function of wind speed. The proposed decision making framework finds the optimal decision variables in a way that they remain robust against the considered uncertainties. To illustrate the effectiveness of the proposed approach, it is applied on the IEEE 118-bus system. The obtained results validate the applicability of the proposed IGDT-based OPF model for optimal operation of AC/DC power systems with high penetration of offshore wind farms. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> Natural disasters such as Hurricane Sandy can seriously disrupt the power grids. To increase the resilience of an electric distribution system against natural disasters, this paper proposes a resilient distribution network planning problem (RDNP) to coordinate the hardening and distributed generation resource allocation with the objective of minimizing the system damage. The problem is formulated as a two-stage robust optimization model. Hardening and distributed generation resource placement are considered in the distribution network planning. A multi-stage and multi-zone based uncertainty set is designed to capture the spatial and temporal dynamics of an uncertain natural disaster as an extension to the traditional ${N}$ - ${K}$ contingency approach. The optimal solution of the RDNP yields a resilient distribution system against natural disasters. Our computational studies on the IEEE distribution test systems validate the effectiveness of the proposed model and reveal that distributed generation is critical in increasing the resilience of a distribution system against natural disasters in the form of microgrids. <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> The presence of uncertain parameters in electrical power systems presents an ongoing problem for system operators and other stakeholders when it comes to making decisions. Determining the most appropriate dispatch schedule or system configuration relies heavily on forecasts for a number of parameters such as demand, generator availability and more recently weather. These uncertain parameters present an even more compelling problem at the distribution level, as these networks are inherently unbalanced, and need to be represented as such for certain tasks. The work in this paper presents an information gap decision theory based three-phase optimal power flow. Assuming that the demand is uncertain, the aim is to provide optimal and robust tap setting and switch decisions over a 24-hour period, while ensuring that the network is operated safely, and that losses are kept within an acceptable range. The formulation is tested on a section of realistic low voltage distribution network with switches and tap changers present. <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> This paper investigates the impacts of integrating nondeterministic flexible ramp reserves in a multistage multiresolution day-ahead robust unit commitment to cope with variability of renewable energy sources. While the common literature considers deterministic forms of flexible ramp reserves, the proposed affinely adjustable robust UC model considers explicit endogenous up and down subhourly ramp reserve deployment to effectively allocate flexible ramp capacities. In addition, unlike the conventional noncausal two-stage robust unit commitment models that immunize solutions against worst economic scenario with the dependency of decisions on the future information of uncertainty, the current nonconservative multistage model optimizes hourly commitment and dispatch scheduling for the base-case scenario, while ensuring that the specified subhourly causal ramp reserves can be adaptively adjusted. Moreover, dimensionality limitations of the conventional duality-based affinely adjustable robust optimizations are resolved. The effectiveness of the proposed model compared with conventional robust unit commitment frameworks is extensively illustrated on the IEEE 24-bus reliability test system and the IEEE 118-bus test system. <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> Distribution expansion planning (DEP) is used to determine the best expansion plan of a distribution system. Considering the uncertainties of loads and of power productions of wind distributed energy resources (DERs) and incorporating AC power flow equations make the DEP problem increasingly challenging. In this context, this paper presents a new adaptive robust distribution expansion planning model to identify the timing of feeder reinforcements in addition to the location, capacity, and installation time of dispatchable and wind DERs. The proposed approach characterizes the uncertain nature of loads and power productions of wind DERs through polyhedral uncertainty sets and the robustness of the solution can be controlled by means of a budget of uncertainty. Since AC power flow equations rather than DC ones are considered, the proposed model is a nonlinear and non-convex min–max–min optimization problem, which cannot be solved directly by commercial optimization packages. Hence, a tri-level decomposition algorithm using both primal and dual cutting planes is introduced to solve the problem. Additionally, to attain a smoother optimization problem, the non-convex AC power flow equations are convexified. The working of the proposed model is illustrated using 33-bus and 123-bus distribution networks. <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> J. ROBUST OPTIMIZATION <s> Electrical distribution systems (EDSs) should be prepared to cope with demand growth in order to provide a quality service. The future increase in electric vehicles (EVs) represents a challenge for the planning of the EDS due to the corresponding increase in the load. Therefore, methods to support the planning of the EDS, considering the uncertainties of conventional loads and EV demand, should be developed. This paper proposes a mixed-integer linear programming (MILP) model to solve the robust multistage joint expansion planning of EDSs and the allocation of EV charging stations (EVCSs). Chance constraints are used in the proposed robust formulation to deal with load uncertainties, guaranteeing the fulfillment of the substation capacity within a specified confidence level. The expansion planning method considers the construction/reinforcement of substations, EVCSs, and circuits, as well as the allocation of distributed generation units and capacitor banks along the different stages in which the planning horizon is divided. The proposed MILP model guarantees optimality by applying classical optimization techniques. The effectiveness and robustness of the proposed method is verified via two distribution systems with 18 and 54 nodes. Additionally, Monte Carlo simulations are carried out, aiming to verify the compliance of the proposed chance constraint. <s> BIB007
Unlike scenario-based optimizations, where PDF information of uncertain parameter is a prerequisite, the robust optimization deals with uncertain parameters using bounded intervals. The two end sides of bounded intervals known as respective ranges are extracted based on a specific confidence interval (CI) range (e.g. 90%, 95%). Robust optimization is a PDF free scenario. However, the optimality of the worst-case solution is retained at the cost of the conservativeness of the results BIB005 . In BIB006 , authors propose a robust DEP model which is adaptively adjustable to uncertainty realizations and can make optimal decisions on sizing and siting of conventional DGs and wind-based DGs with feeder installation schedules. In BIB007 , DEP and PEV charging allocation are considered jointly in a robust optimization framework against the uncertainty of electricity demand modelled with polyhedral uncertainty set. Authors in BIB003 proposed resilience enhancement in a DEP problem using robust optimization and minimizing the damage to the distribution system in the face of natural disasters. The information gap decision theory (IGDT) is a more specific feature of robust optimization which evaluates the deviations between the realizations and the approximations of the uncertain parameters. A risk-averse decision maker maximizes robustness function to decide robustly against unfavourable disparities of the uncertain parameter. Oppositely, a Risk-seeker decision-maker minimizes Opportunity function to benefit from the favourable divergence of the uncertain parameter from the expected value BIB004 . The method is applied to solve DEP in many papers. For instance, in BIB001 , DEP is solved while considering uncertainties from renewable generations and load. The authors in BIB002 investigate voltage stability, and voltage congestion constrained problems in distribution systems with a high penetration rate of renewables using IGDT platform.