Although Voltage Varying (VV) strategies like Conservation Voltage Reduction (CVR) are widely used by utilities to reduce the overall energy consumption and peak power demand of distribution feeders, it is aberrant among industrial customers. This research proposes a Voltage Varying (VV) strategy for industrial customers that takes into account their complex characteristics and unique set of constraints. Unlike VV strategies for Local Distribution Companies (LDC), those for an industrial customers are far more complex, and require specific c load modelling and process estimation to infer the optimal operating voltage for the industrial load. The proposed VV technique referred to as Voltage Optimization (VO), is a generic and comprehensive framework that seeks to reduce the energy consumption of the industrial load vis-~a-vis the bus voltage. It utilizes a Neural Network (NN) model of the industrial load, trained using historical operating data, to estimate the real power consumption of the load, based on the bus voltage and overall plant process. This load model, is incorporated into the proposed VO model, whose objective is the minimization of the energy drawn from the substation and the switching operations of Load Tap Changers (LTC). The proposed VO framework is tested on load models developed using simulated and real data. Results suggest that the proposed technique can be successfully implemented by industrial customers or plant operators to improve their energy savings, in comparison to existing VV techniques.

Although Voltage Varying (VV) strategies like Conservation Voltage Reduction (CVR) are widely used by utilities to reduce the overall energy consumption and peak power demand of distribution feeders, it is aberrant among industrial customers. This research proposes a Voltage Varying (VV) strategy for industrial customers that takes into account their complex characteristics and unique set of constraints. Unlike VV strategies for Local Distribution Companies (LDC), those for an industrial customers are far more complex, and require specific c load modelling and process estimation to infer the optimal operating voltage for the industrial load. The proposed VV technique referred to as Voltage Optimization (VO), is a generic and comprehensive framework that seeks to reduce the energy consumption of the industrial load vis-~a-vis the bus voltage. It utilizes a Neural Network (NN) model of the industrial load, trained using historical operating data, to estimate the real power consumption of the load, based on the bus voltage and overall plant process. This load model, is incorporated into the proposed VO model, whose objective is the minimization of the energy drawn from the substation and the switching operations of Load Tap Changers (LTC). The proposed VO framework is tested on load models developed using simulated and real data. Results suggest that the proposed technique can be successfully implemented by industrial customers or plant operators to improve their energy savings, in comparison to existing VV techniques.

Electric power distribution systems are gradually adopting new advancements in communication, control, measurement, and metering technologies to help realize the evolving concept of Smart Grids. Future distribution systems will facilitate increased and active participation of customers in Demand Side Management activities, with customer load profiles being primarily governed by real-time information such as energy price, emission, and incentive signals from utilities. In such an environment, new mathematical modeling approaches would allow Local Distribution Companies (LDCs) and customers the optimal operation of distribution systems and customer's loads, considering various relevant objectives and constraints. This thesis presents a mathematical model for optimal and real-time operation of distribution systems. Thus, a three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which incorporates comprehensive and realistic models of relevant distribution system components. A novel optimization objective, which minimizes the energy purchased from the external grid while limiting the number of switching operations of control equipment, is considered. A heuristic method is proposed to solve the DOPF model, which is based on a quadratic penalty approach to reduce the computational burden so as to make the solution process suitable for real-time applications. A Genetic Algorithm based solution method is also implemented to compare and benchmark the performance of the proposed heuristic solution method. The results of applying the DOPF model and the solution methods to two distribution systems, i.e., the IEEE 13-node test feeder and a Hydro One distribution feeder, are discussed. The results demonstrate that the proposed three-phase DOPF model and the heuristic solution method may yield some benefits to the LDCs in real-time optimal operation of distribution systems in the context of Smart Grids. This work also presents a mathematical model for optimal and real-time control of customer electricity usage, which can be readily integrated by industrial customers into their Energy Hub Management Systems (EHMSs). An Optimal Industrial Load Management (OILM) model is proposed, which minimizes energy costs and/or demand charges, considering comprehensive models of industrial processes, process interdependencies, storage units, process operating constraints, production requirements, and other relevant constraints. The OILM is integrated with the DOPF model to incorporate operating constraints required by the LDC system operator, thus combining voltage optimization with load control for additional benefits. The OILM model is applied to two industrial customers, i.e., a flour mill and a water pumping facility, and the results demonstrate the benefits to the industrial customers and LDCs that can be obtained by deploying the proposed OILM and three-phase DOPF models in EHMSs, in conjunction with Smart Grid technologies.