Implementation and assessment of demand response and voltage/var control with distributed generators

The main topic of this research is the efficient operation of a modernized distribution grid from both the customer side and utility side. For the customer side, this dissertation discusses the planning and operation of a customer with multiple demand response programs, energy storage systems and distributed generators; for the utility side, this dissertation addresses the implementation and assessment of voltage/VAR control and conservation voltage reduction in a distribution grid with distributed generators. The objectives of this research are as follows: (1) to develop methods to assist customers to select appropriate demand response programs considering the integration of energy storage systems and DGs, and perform corresponding energy management including dispatches of loads, energy storage systems, and DGs; (2) to develop stochastic voltage/VAR control techniques for distribution grids with renewable DGs; (3) to develop optimization and validation methods for the planning of integration of renewable DGs to assist the implementation of voltage/VAR control; and (4) to develop techniques to assess load-reduction effects of voltage/VAR control and conservation voltage reduction. In this dissertation, a two-stage co-optimization method for the planning and energy management of a customer with demand response programs is proposed. The first level is to optimally select suitable demand response programs to join and integrate batteries, and the second level is to schedule the dispatches of loads, batteries and fossil-fired backup generators. The proposed method considers various demand response programs, demand scenarios and customer types. It can provide guidance to a customer to make the most beneficial decisions in an electricity market with multiple demand response programs. For the implementation of voltage/VAR control, this dissertation proposes a stochastic rolling horizon optimization-based method to conduct optimal dispatches of voltage/VAR control devices such as on-load tap changers and capacitor banks. The uncertainties of renewable DG output are taken into account by the stochastic formulation and the generated scenarios. The exponential load models are applied to capture the load behaviors of various types of customers. A new method to simultaneously consider the integration of DGs and the implementation of voltage/VAR control is also developed. The proposed method includes both solution and validation stages. The planning problem is formulated as a bi-level stochastic program. The solution stage is based on sample average approximation (SAA), and the validation stage is based on multiple replication procedure (MRP) to test the robustness of the sample average approximation solutions of the stochastic program. This research applies big data-driven analytics and load modeling techniques to propose two novel methodologies to assess the load-reduction effects of conservation voltage reduction. The proposed methods can be used to assist utilities to select preferable feeders to implement conservation voltage reduction.Ph.D

Building Energy Modeling and Studies of Electric Power Distribution Systems with Distributed Energy Resources

There is significant opportunity for savings in energy and investment from improved performance of electric Power Distribution Systems (PDSs) through optimal planning and operation of conventional voltage-controlling devices. Novel multi-step model conversion and optimal capacitor planning (OCP) procedures are proposed for large-scale utility PDSs and are exemplified with an existing utility circuit of approximately 4,000 buses. Simulated optimal control and operation is achieved with a cluster-based approach that utilizes load-forecasting to minimize equipment degradation by intelligently dispersing device setting adjustments over time such that they remain most applicable. Improved performance may also be achieved through smart building technologies and Virtual Power Plant (VPP) control of increasingly more prevalent Distributed Energy Resources (DERs). The established simulation test bed for PDSs incorporates DERs to evaluate VPP implementations and an optimization process for control timing is proposed that minimizes targeted peak power and possible resulting increase in total daily energy. The advanced VPP controls incorporate the Consumer Technology Association (CTA) 2045 standard and EnergyStar performance characterizations to leverage HVAC systems as Generalized Energy Storage (GES) for load manipulation and to support the integration of demand-side generating DERs, such as local solar Photo-Voltaic (PV) systems

Improving Energy Efficiency and Productivity in Industrial Plants Using Dynamic Voltage Management

Conservation voltage reduction (CVR) is an established practice of having the distribution system loads operate at the lower end of the accepted limits based on the standard ANSI C84.1 to provide reduction in power consumption. This standard stipulates optimal voltage levels where the customer equipment is expected to operate with normal performance. CVR has been implemented traditionally by utilities by controlling the voltage on a distribution circuit to the lower end of the tolerance band defined by this standard. The effects of conservation voltage reduction are quantified by a metric called CVR factor (CVR_f). Some references have pointed out that utilities may have a concern about potential reduction in revenue they might incur due to conservation voltage reduction. CVR factor on an average at the distribution feeder level has been discussed to be 0.8. This research investigates the mechanism of saving energy in industrial plants and suggests measures to quantify the CVR factor. The requirements of a device for voltage management are explained. The proposed solution employs coaxial winding transformers whose salient features have been discussed. Existing solutions may have significant issues. Their limited fault handling capability makes them less robust to handle the forces and this may lead to destruction of the equipment or they may experience other issues including sluggish response or low efficiency.Ph.D