Overview of Electric Energy Distribution Networks Expansion Planning <s> F. EMERGING ACTIVE DISTRIBUTION NETWORKS <s> Abstract This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> F. EMERGING ACTIVE DISTRIBUTION NETWORKS <s> Abstract The intermittent generation of non-dispatchable renewable distributed generation, along with the load variability, demand growth and electricity market prices impose operational uncertainties on the distribution network planning. The accurate and efficient modelling of these uncertainties is essential to guarantee the optimal integration of distributed energy resources in the distribution systems. This work firstly gives an overview of the generic distribution network planning and active distribution network planning. Then, the basic framework and the key features of the active distribution network planning are examined. Existing literature in the uncertainty modelling techniques is reviewed, discussed, and categorised into the following: probabilistic techniques, stochastic optimisation, robust optimisation, possibilistic techniques, hybrid probabilistic–possibilistic techniques and information gap decision theory. The typical active distribution network planning problems include the distributed generation investment planning, optimal storage allocation, reliability assessment, probabilistic optimal power flow, optimal reactive power planning, substation expansion and feeder reconfiguration. A systematic summary and comparative evaluation of the uncertainty modelling techniques is provided, showing that there is no single best uncertainty modelling technique so far, and the choice of an appropriate technique depends upon the type of uncertain input variables and planning problem. This is followed by a discussion of additional challenges and their solutions in the context of active distribution network planning. <s> BIB002
Recent developments in operational controlling and monitoring schemes such as advanced control techniques, complicated information and communication technologies, along with emerging multi-career energy systems (MCE) and demand response programs, passive dummy distribution networks have been changed to active and dynamic systems in the form of Active Distribution Networks (ADNs). Accordingly, ADN planning (ADNP) in contrast with conventional DEP comes with new objectives and concepts BIB002 . Due to the low penetration rate of DERs in conventional distribution networks, coordinated planning and operation was not an obligation, and the uncertainty risk analysis was not a necessity. However, conventional DEPs conduct a fitand-forget method based on deterministic frameworks. An ADNP shall coordinate 1) planning and operation, 2) run probabilistic planning (in replace of deterministic one), 3) integrate uncertainty risk control methods, and 4) consider multi-time resolution modelling to respond to the high penetration rate of DERs in ADNs BIB001 . Big data resource management may be required to deal with real-time measurement and information received by several sensors in the distribution network, weather data and consumption behaviour of customers. Generally, ADNP considers allocating new optimization framework.