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With deregulation of the power industry, environmental policy changes, advancements in technology, and the transformation to smart grid, the distribution planning paradigm has gone through significant changes in recent years. Concurrently, with increase in gas prices, driven by a foreseeable fossil fuel depletion in the future, developments in the automotive sector, and environmental concerns, penetration of plug-in electric vehicles (PEVs) has been increasing. These changes will continue to drive the distribution planning problem to evolve in the coming years. This paper presents a comprehensive long-term distribution planning framework from the perspective of local distribution companies (LDCs) considering distributed generation (DG), substations, capacitors, and feeders. Apart from considering the usual demand profile, the proposed framework considers uncontrolled and controlled (smart) PEV charging demand, as well as demand response (DR) options. Based on a back-propagation algorithm combined with cost-benefit analysis, a novel approach is proposed to determine the optimal upgrade plan, allocation, and sizing of the selected components in distribution systems, to minimize the total capital and operating cost. A new iterative method is proposed which involves post-processing the plan decisions to guarantee acceptable adequacy levels for each year of the planning horizon. The performance of the proposed framework is examined considering several case studies on the 33-bus and 69-bus test systems. It is noted from the studies that the presence of uncontrolled PEV charging loads results in much higher plan costs as compared to the case without PEVs, impacts the distribution plan significantly, and hence these loads indeed need be considered in the planning process. On the other hand, smart charging of PEVs have a much reduced impact on the plan cost, and helps alleviate some investments which were needed with uncontrolled charging PEVs. Therefore, the LDCs and policy makers need to encourage customers to adapt these options in the long-run. [ABSTRACT FROM AUTHOR]

In deregulated electricity sector climates, such as in Ontario, the production of clean or renewable energy by small power producers through distributed generation (DG) is encouraged. This paper examines the policies that can be used to encourage DG investment and incorporates them into a mathematical model. This model is then used to create scenarios for examining the economic and environmental supply-side effects of policies to a distribution system over a ten-year period. The policies analyzed include a combination of feed-in-tariffs, CO2 tax, and cap-and-trade schemes. The results are discussed in the context of the Ontario market and its Standard Offer Program, implemented on a 32-bus radial distribution system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel schematic approach for coordinating the selection of distributed generation unit investment proposals submitted by multiple, competing, private investors to achieve maximum investor participation while complying with the technical operational limits of the local distribution company. The concept of domains and commons has been used to identify the largest transgressors to system function, and forms the basis for revisions to the investor proposals. [ABSTRACT FROM AUTHOR]

This paper presents a new paradigm for distribution system operation in the presence of distributed generation (DG) sources taking into consideration the goodness factor of the DG units. The proposed concept of goodness factor of DG units is based on the computation of the incremental contribution of a DG unit to distribution system losses. The incremental contributions of a DG unit to active and reactive power losses in the distribution system are termed as the active/reactive incremental loss indices (ILI). The goodness factors are integrated directly into the distribution system operations model, which is based on an optimal power flow (OPF) framework. This model seeks to minimize the distribution company's (disco's) energy costs in the short term taking into account the contribution (goodness factor) of each DG unit. Two scenarios are considered in the paper: the first scenario considers the disco to be the owner of the DG units and hence is responsible for their scheduling and dispatch, and the second scenario considers the DG units to be investor-owned. The analysis was carried out considering an 18-bus distribution network extracted from the well-known IEEE 30-bus system and a 69-bus distribution system. [ABSTRACT FROM AUTHOR]

A generic operations framework for a distribution company (disco) operating in a competitive electricity market environment is presented in this paper. The operations framework is a two-stage hierarchical model in which the first deals with disco's activities in the day-ahead stage, the Day Ahead Operations Model (DAOM). The second deals with disco's activities in real-time and is termed Real-Time Operations Model (RTOM). The DAOM determines the disco's operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco's actions and decision-making in this context. [ABSTRACT FROM AUTHOR]

This paper proposes a new heuristic approach for distributed generation (DC) capacity investment planning from the perspective of a distribution company (disco). Optimal sizing and sitting decisions for DC capacity is obtained through a cost-benefit analysis approach based on a new optimization model. The model aims to minimize the disco's investment and operating costs as well as payment toward loss compensation. Bus-wise cost-benefit analysis is carried out on an hourly basis for different forecasted peak demand and market price scenarios. This approach arrives at the optimal feasible DC capacity investment plan under competitive electricity market auction as well as fixed bilateral contract scenarios. The proposed heuristic method helps alleviate the use of binary variables in the optimization model thus easing the computational burden substantially. [ABSTRACT FROM AUTHOR]