An NSGA-II based Multi-Objective Approach for Distribution System Voltage Control

The aim of this work is to offer a voltage control strategy for distribution networks that experience voltage unbalance due to single phase and unbalanced loads and voltage rise due to high penetration of Distributed Generation units. The objectives are minimization of voltage imbalance on each node and total power losses on the entire network. The control of node voltages by Distributed Generation units has potential to clash with the more traditional method of voltage control adopted by Distribution Network Operators namely, tap changing voltage regulators and shunt capacitors. We look at a coordinated method of voltage control that solves the multi-objective optimization problem of voltage profile improvement and power loss reduction using a Pareto optimal and elitist evolutionary optimization algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II). The study system is the IEEE 123 bus distribution test feeder which is highly unbalanced and includes most of the elements of a real network

Novel Volt/Var Control Strategies for Active Distribution Systems

Power distribution networks are rapidly evolving as active distribution systems, as a result of growing concerns for the environment and the shift towards renewable energy sources (RESs). The introduction of distributed generations can benefit the distribution network in terms of voltage support, loss reduction, equipment capacity release, and greenhouse gas (GHG) emission reduction. However, the integration of RESs into electric grids comes with significant challenges. The produced energy from renewable sources such as wind and solar is intermittent, non-dispatchable and uncertain. The uncertainty in the forecasted renewable energy will consequently impact the operation and control of the power distribution system. The impact on Volt/Var control (VVC) in active distribution systems is of particular concern, mainly because of reverse power flow caused predominantly by RESs. RESs can influence the operation of voltage control devices such as on-load tap changers (OLTCs), line voltage regulators (VRs) and shunt capacitor banks (ShCs). It is mainly because of reverse power flow, caused predominantly by RESs. Reverse power flow or injecting power between the regulator and the regulation point can confuse the local regulator controller, which leads to inappropriate or excessive operations. Some of the potential adverse effects include control interactions, operational conflicts, voltage drop and rise cases at different buses in a network. This research project aims to carry out an in-depth study on coordinated Volt/Var control strategies in active distribution networks. The thesis focuses on the problem of Volt/Var optimization in active distribution networks, operated under different operating conditions, by taking into consideration the current distribution system requirements and challenges in the presence of high RESs penetration. In the initial phase of the research project, a generic solution to the VVC problem of active distribution systems was first developed. The primary goal of this generic solution involved the determination of an optimal control strategy based on system status, which was identified from bus voltages. As such, there are three different operating states; normal, intermediate and emergency state. Each operating state has its own control strategy that includes state-related objective functions, such as minimization of power losses, operational control costs, and voltage deviation. For both normal and intermediate state operations, a heuristic-search based optimization algorithm is implemented. In order to be able to take control actions rapidly, a novel rule-based control strategy is developed for the emergency state. In the second phase of the research project, the proposed zone-oriented convex distributed VVC algorithm was developed to address the limitations of heuristic optimization algorithms, including long solution times and the non-global optimal solution. The proposed algorithm is based on chordal-relaxation semi-definite programming (SDP), and divides distribution systems into areas based on customer types, wherein, each zone has its own priorities, characteristics, and requirements. The primary goal is to achieve optimal voltage control for each zone, according to its operational requirements and characteristics. Furthermore, in contrast to many decentralized approaches that require iterative solutions to update global multiplier and a penalty parameter to convergence, this method proposes a novel multi-period hierarchical convex distributed control algorithm, requiring no iterative process and no penalty parameter. Eliminating the iterative solution makes convergence fast, while having no penalty parameter allows for the algorithm to be less human and system dependent. In the final phase of the research project, a 2-stage control algorithm aiming to minimize VR tap movements in convex VVC formulation was developed. In the first stage, the VVC problem is solved for hourly intervals, and VR tap positions are obtained. In the second stage, control horizon is divided into 15 minutes intervals, and the voltage is controlled only by the RESs' active and reactive power adjustment. The tap movement minimization and 2-stage control algorithm eliminates the excessive use of VRs, prolongs the operational life of VRs and reduces the system operational cost. The optimal operation of Volt/Var control devices was investigated in the presented Volt/Var optimization methodology. The proposed research will pave the way for managing the increasing penetration of RESs with different types, technologies and operational modes, from a distribution system voltage control perspective. The proposed methodologies in this thesis have been tested on sample distribution systems and their effectiveness is validated

Planning and Operation Framework of Smart Distributed Energy Resources in Emerging Distribution Systems

Smart grid technologies provoke a major paradigm shift in how power systems are planned and operated. The transition to a smarter system is happening gradually; however, researchers has reported that this transition is dealt with generally in a "react and provide" manner. Proper planning studies allow for this transition to be done in a predict and provide" fashion. Part of planning this transition is envisioning the future system. Technical, social, environmental, and economical challenges are foreseen and tackled in the literature; however, no generalized planning framework that addresses the overall picture of different involved parties' interests and their anticipated (and often conflicting) interactions to properly plan for a better and fair outcome has been developed. This work addresses the issue in several steps: 1) it provides a backbone framework architecture for asset sizing and allocation in the future Smart Distribution System (SDS), 2) it considers the daily optimal operation of these assets in the long-term planning problem, and 3) it considers the potential conflicts that happen on the long-term planning level and operational levels. This architecture requires the development of a framework capable of absorbing private investments, integrating new technologies, promoting smart grid applications, and yet remaining feasible to all involved parties. Strategic analysis of the involvement of each stakeholder has been conducted. Proceeding from this analysis, deductions and conclusions about venues for promoting and allowing a smoother transition to the new paradigm are drawn. In addition, this analysis highlighted potential conflicts that are showcased in two different case studies. Potential ways in which the conditions affect the planning procedure and how they can be overcome are proposed. The recommendations can be highlighted as follows: 1) promoting new smart grid technologies, 2) encouraging communications and cooperation between involved parties, 3) considering daily optimal operation of assets to fully take advantage of their new active nature to better allocate them in the long-term planning problem, and 4) consideration of stakeholders interests in the planning phase in order to better absorb investments and move to the new paradigm. To size and allocate assets in the long-term planning problem for the SDS, first a building algorithm has been developed to size and allocate DG units. This algorithm breaks the problem into two subproblems to overcome the modeling and computational challenges of the mixed-integer nonlinear programming problem. The first subproblem is addressed using heuristic optimization techniques, namely a genetic algorithm, and the second involving deterministic analytical means of nonlinear optimization, utilizing the advancements made in branch-and-bound methods providing a proven global optimal solution to non-convex problems. Considering the daily optimal operation and both electric utilities' and investors' objectives, the planning problem has been developed. The results show greater private investments absorption, reduced costs to both parties, and higher system performance due to lowered energy losses. The expected increased numbers of customers opting to become resilient and have a more reliable service pose several operational and planning challenges. In this work, a novel consensus-based algorithm is introduced as an economically efficient tool for coordinating prosumers' interactions, within the feasible solution region. Several objectives are targeted in this work, among which the global economic benefit maximization of all interacting prosumers is the most salient. This economic benefit comprises the total cooperative payoff of the interacting prosumers. Each prosumer has its own private bounds defining the range of power production and consumption. A novel definition is proposed for prosumers' interaction in the hybrid microgrids. The developed scheme's importance stems from the dramatic change in the smart networks' paradigms. In addition, the individual prosumers' preferences are recognized via the comprehensive mathematical modeling for the evolved AC/DC network. The results are provided for a basic two-prosumer scenario. However, these results highlight the potential of the proposed approach in a practical system setting. More sophisticated case studies, i.e., multi-power levels, multi-prosumers, and different system topologies, could be also studied using the proposed work

Online Assessment of Distributed Generation Connection for Smart Grid

Increasing renewable energy generation is among the most important objectives of smart grid, especially due to the increased environmental concerns, energy demand, and depletion of fossil energy resources. Introducing incentive feed-in tariff (FIT) programs to promote renewable distributed generation (DG) in distribution systems is an essential step towards smart grid implementation. However, current regulations of FIT programs for small-scale DG sources strictly limit the aggregated installed DG capacity to a small fraction of the system peak load. Limiting the DG capacity avoids the need for detailed connection impact assessment studies for the DG connection. Conducting detailed CIA studies for each small-scale DG project application is impractical due to the large number of applications, which can lead to delaying the DG connection process. However, avoiding assessment studies and imposing such strict limits result in rejecting numerous applications for renewable DG projects, and therefore losing a significant amount of renewable DG capacity. Such situations underscore the need for research that suggests new directions for increasing small-scale renewable DG projects under FIT programs. In order to accomplish this target, this thesis presents a planning model and a management scheme for DG connection online assessment in smart grids. The planning model achieves two objectives: insuring an adequate profit for DG owners and maximizing the number of installed DG sources in the systems. The management scheme controls the curtailment of the connected DG units to satisfy the system operational constrains. Implementing the proposed work evades the need for detailed connection impact assessment studies prior to installing small-scale DG units since the assessment is performed on an online basis. This feature can therefore reduce the number of rejected applications for renewable DG projects under FIT programs while accelerating the DG connection process. The proposed planning model and management scheme for DG connection online assessment are based on dividing the output power of each DG unit into two components: unconditional and conditional. The unconditional DG component refers to the portion of DG output power that is not subject to curtailment for all online conditions of the system; this component guarantees an adequate profit for the DG investors. The conditional DG component denotes the portion of the DG output power that is subject to curtailment. The curtailment of the conditional DG component is controlled using the proposed management scheme for DG connection online assessment. The first phase of this work introduces an economic model for calculating the unconditional DG component. This model ensures that the unconditional DG component, which is not susceptible to curtailment, yields adequate profit for DG investors. The first part also presents a techno-economic planning model that maximizes the number of DG units installed based on the technical and economic constraints. The second phase of this work presents a novel algorithm for DLF analysis that can interact with the continual changes of load and network topology in smart grids. This algorithm can solve the DLF problem in a specific area of interest in a distribution system without necessitating the inclusion of all of the system buses. This ``zooming'' feature leads to a significant reduction in the required DLF solution time, especially for large distribution systems. This DLF algorithm is utilized in obtaining load flow results in the proposed management scheme for DG connection online assessment, presented in the third phase of this work. The third phase of this work introduces a management scheme for DG connection online assessment in smart grids. The assessment is performed using a novel scalable optimization model that utilizes the ``zooming'' feature of the proposed DLF algorithm, presented in the second phase of this work. The scalable optimization model can therefore minimize the curtailment of the conditional DG components in a specific area of interest in the system without including all the system buses in the optimization problem. This feature ensures fast calculation of the minimum DG power to be curtailed based on the online condition of the system. The simulation results include a comparison between two maximum renewable DG capacities - that which can be installed according to the current FIT rules in Ontario and that which can be installed by implementing the proposed planning model with the management scheme for DG connection online assessment. The comparison indicates that implementing the proposed work would significantly increase the number of small-scale renewable DG projects that can be installed

Zonal Energy Management and Optimization System (ZEMOS) for Smart Grid Applications

In the context of implementing the smart grid, electric energy consumption, generation resources, should be managed and optimized in a way that saves energy, improves efficiency, enhances reliability and maintains security while meeting the increasing demand at minimum operating cost. In order to achieve these objectives, there is a need to implement an efficient Zonal Energy Management and Optimization Systems (ZEMOS) that address both existing and future challenges possibly imposed by the use of renewable energy generators that lead to bi-directional power flow instead of unidirectional as in the traditional grids while operate in a coordinated way for the benefit of the whole electric grid. The proposed ZEMOS contains custom defined built-in functions in modular form, which could easily be integrated with other existing energy monitoring systems in the zone of interest (i.e. industrial facility, commercial centers, testing facility, sub-system of the utility service area, educational institutions, power plant, etc.). The proposed ZEMOS provides functions that ensure energy saving, improved reliability, increased efficiency and enhanced utilization of distributed resources: generation energy storage and loads without compromising the tasks carried within that zone. Those module-based systems are characterized by their scalability and flexibility, since more functions can be added down the road as needed. This is necessary in order to accommodate the constant changes imposed by the smart grid and avoid the need to change the whole infrastructure. The proposed ZEMOS performance was investigated for study zones that involve single and multi-objective operations. Besides, study zones with more than single decision makers were also considered in this thesis. Accordingly, the implementation of ZEMOS satisfies the outlined objectives for specific study zone which leads to a reduction in greenhouse gas emission, the improvement of the energy generation portfolio, a reliance on the optimized renewable energy source and a reduction in the energy losses while ensuring high power quality. Furthermore, managing the energy consumption and optimizing the operation of such sizable zones (at Mega Watts scale) ensures significant economic benefits in terms of energy saving, better utilization of available resources, improving the efficiency of energy systems, and exporting novel smart grid technologies, which will lay the foundation to meet future challenges using existing infrastructure

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

Optimum Distribution System Architectures for Efficient Operation of Hybrid AC/DC Power Systems Involving Energy Storage and Pulsed Loads

After more than a century of the ultimate dominance of AC in distribution systems, DC distribution is being re-considered. However, the advantages of AC systems cannot be omitted. This is mainly due to the cheap and efficient means of generation provided by the synchronous AC machines and voltage stepping up/down allowed by the AC transformers. As an intermediate solution, hybrid AC/DC distribution systems or microgrids are proposed. This hybridization of distribution systems, incorporation of heterogeneous mix of energy sources, and introducing Pulsed Power Loads (PPL) together add more complications and challenges to the design problem of distribution systems. In this dissertation, a comprehensive multi-objective optimization approach is presented to determine the optimal design of the AC/DC distribution system architecture. The mathematical formulation of a multi-objective optimal power flow problem based on the sequential power flow method and the Pareto concept is developed and discussed. The outcome of this approach is to answer the following questions: 1) the optimal size and location of energy storage (ES) in the AC/DC distribution system, 2) optimal location of the PPLs, 3) optimal point of common coupling (PCC) between the AC and DC sides of the network, and 4) optimal network connectivity. These parameters are to be optimized to design a distribution architecture that supplies the PPLs, while fulfilling the safe operation constraints and the related standard limitations. The optimization problem is NP-hard, mixed integer and combinatorial with nonlinear constraints. Four objectives are involved in the problem: minimizing the voltage deviation (ΔV), minimizing frequency deviation (Δf), minimizing the active power losses in the distribution system and minimizing the energy storage weight. The last objective is considered in the context of ship power systems, where the equipment’s weight and size are restricted. The utilization of Hybrid Energy Storage Systems (HESS) in PPL applications is investigated. The design, hardware implementation and performance evaluation of an advanced – low cost Modular Energy Storage regulator (MESR) to efficiently integrate ES to the DC bus are depicted. MESR provides a set of unique features: 1) It is capable of controlling each individual unit within a series/parallel array (i.e. each single unit can be treated, controlled and monitored separately from the others), 2) It is able to charge some units within an ES array while other units continue to serve the load, 3) Balance the SoC without the need for power electronic converters, and 4) It is able to electrically disconnect a unit and allow the operator to perform the required maintenance or replacement without affecting the performance of the whole array. A low speed flywheel Energy Storage System (FESS) is designed and implemented to be used as an energy reservoir in PPL applications. The system was based on a separately excited DC machine and a bi-directional Buck-Boost converter as the driver to control the charging/discharging of the flywheel. Stable control loops were designed to charge the FESS off the pulse and discharge on the pulse. All the developments in this dissertation were experimentally verified at the Smart Grid Testbed