Generation asset planning under uncertainty.

With the introduction of competition in the electric power industry, generation asset planning must change. In this changed environment, energy companies must be able to capture the extrinsic value of their asset operations and long-term managerial flexibility for sound planning decisions. This dissertation presents a new formulation for the generation asset planning problem under market uncertainty, in which short-term operational and long-term coupling constraints associated with investment decisions are simultaneously reflected in the planning process

Scenario generation and reduction for long-term and short-term power system generation planning under uncertainties

This dissertation focuses on computational issues of applying two-stage stochastic programming for long-term and short-term generation planning problems from the perspective of scenario generation and reduction. It follows a three-paper format, in which each paper discusses approaches to generating probabilistic scenarios and then reducing the substantial computational burden caused by a huge number of scenarios for different applications in power systems. The first paper investigates a long-term generation expansion planning model with uncertain annual load and natural gas price. A two-stage stochastic program is formulated to minimize the total expected expansion cost, generation cost and penalties on unserved energy while satisfying aggregated operational constraints. A statistical property matching technique is applied to simulate plausible future realizations of annual load and natural gas price over the whole planning horizon. To mitigate the computational complexity of a widely used classic scenario reduction method in this context, we firstly cluster scenarios according to the wait-and-see solution for each scenario and then apply the fast forward selection (FFS) method. The second paper prepares a basis for load scenario generation for the day-ahead reliability unit commitment problem. For the purpose of creating practical load scenarios, epi-splines, based on approximation theory, are employed to approximate the relationship between load and weather forecasts. The epi-spline based short-term load model starts by classifying similar days according to daily forecast temperature as well as monthly and daily load patterns. Parameters of the epi-spline based short-term load model are then estimated by minimizing the fitted errors. The method is tested using day-ahead weather forecast and hourly load data obtained from an Independent System Operator in the U.S. By considering the non-weather dependent load pattern in the short-term load model, the model not only provides accurate load predictions and smaller prediction variances in the validated days, but also preserves similar intraday serial correlations among hourly forecast loads to those from actual load. The last paper in this dissertation proposes a solution-sensitivity based heuristic scenario reduction method, called forward selection in recourse clusters (FSRC), for a two-stage stochastic day-ahead reliability unit commitment model. FSRC alleviates the computational burden of solving the stochastic program by selecting scenarios based on their cost and reliability impacts. In addition, the variant of pre-categorizing scenarios improves the computational efficiency of FSRC by simplifying the clustering procedure. In a case study down-sampled from an Independent System Operator in the U.S., FSRC is shown to provide reliable commitment strategies and preserve solution quality even when the reduction is substantial

Incentive-Based Expansion Planning and Reliability Enhancement Models for Smart Distribution Systems

Due to the rapid progress toward the implementation of smart grid technologies, electric power distribution systems are undergoing profound structural and operational changes. Climate concerns, a reduction in dependency on fossil fuel as a primary generation source, and the enhancement of existing networks constitute the key factors in the shift toward smart grid application, a shift that has, in fact, already led power industry stakeholders to promote more efficient network technologies and regulation. The results of these advances are encouraging with regard to the deployment and integration of small-scale power generation units, known as distributed generation units (DGs), within distribution networks. DGs are capable of contributing to the powering of the grid from distribution or even sub-distribution systems, providing both a positive effect on network performance and the least adverse impact on the environment. Smart grid deployment has also facilitated the integration of a variety of investor assets into power distribution systems, with a consequent necessity for positive and active interaction between those investors and local distribution companies (LDCs). This thesis proposes a novel incentive-based distribution system planning (IDSP) model that enables an LDC and DG investors to work collaboratively for their mutual benefit. Using the proposed model, the LDC would establish a bus-wise incentive program (BWIP) based on long-term contracts, which would encourage DG investors to integrate their projects at the specific system buses that would benefit both parties. The model guarantees that the LDC will incur minimum expansion and operation costs while concurrently ensuring the feasibility of DG investors’ projects. The proposed model also provides the LDC with the opportunity to identify the least-cost solution among a combination of the proposed BWIP and traditional expansion options (i.e., upgrading or constructing new substations, upgrading or constructing new lines, and/or reconfiguring the system). In this way, the model facilitates the effective coordination of future LDC expansion projects with DG investors. To derive appropriate incentives for each project, the model enforces a number of economic metrics, including the internal rate of return, the profit-investment ratio, and the discounted payback period. All investment plans committed to by the LDC and the DG investors for the full extent of the planning period are then coordinated accordingly. The intermittent nature of both system demand and wind- and PV-based DG output power is handled probabilistically, and a number of DG technologies are taken into account. Several linearization approaches are applied in order to convert the proposed model into a mixed integer linear programming (MILP) model, which is solved using a CPLEX solver. Reliability of service in a deregulated power environment is considered a major factor in the evaluation of the performance of service providers by consumers and system regulators. Adhering to imposed obligations related to the enhancement of overall system reliability places a substantial burden on the planning engineer with respect to investigating multiple alternatives and evaluating each option from both a technical and an economical perspective. This thesis also proposes a value-based reinforcement planning model for improving system reliability while maintaining reliability metrics within allowable limits. The optimal allocation of tie lines and normally open switches is determined by this planning model, along with required capacity upgrades for substations and lines. Two hierarchical levels for system operation under contingencies, namely, the restoration process and islanding-based modes, are applied in the model. A probabilistic analytical model is proposed for computing distribution system reliability indices based on consideration of these two hierarchical operating levels and taking into account variations in system demand, DG output power, and the uncertainty associated with system components. Due to the nature and complexity of these kinds of problems, a metaheuristic technique based on a genetic algorithm (GA) is implemented for solving this model. This thesis also proposes a new iterative planning model for smart distribution systems in which system reliability is considered a primary component in the setting of incentive prices for DG owners. A new concept, called generation sufficiency for dynamic virtual zones, is introduced in the model as a means of enhancing reliability in areas that are subject to reliability issues. To avoid any contravention of operational security boundaries, DG capacity is represented by two components: normal DG operating capacity and reserve DG capacity. The MILP planning model is constructed in a GAMS environment and solved with the use of a CPLEX solver

Management Of Plug-In Electric Vehicles And Renewable Energy Sources In Active Distribution Networks

Near 160 million customers in the U.S.A. are served via distribution networks (DNs). The increasing penetration level of renewable energy sources (RES) and plug-in electric vehicles (PEVs), the implementation of smart distribution technologies such as advanced metering/monitoring infrastructure, and the adoption of smart appliances, have changed distribution networks from passive to active. The next-generation of DNs should be efficient and optimized system-wide, highly reliable and robust, and capable of effectively managing highly-penetrated PEVs, RES and other controllable loads. To meet new challenges, the next-generation DNs need active distribution management (ADM). In this thesis, we study the management of PEVs and RES in active DNs. First, we propose a novel discrete-event modeling method to model PEVs and other loads in distribution networks. In addition, a new optimization algorithm to integrate as many PEVs as possible in DNs without causing voltage issues, including the violation of voltage security ranges and voltage stability, is studied. To further explore the active management of PEVs in the DNs, we develop a universal demonstration platform, consisting of software packages and hardware remote terminal units. The demonstration platform is designed with the capabilities of measurement, monitoring, control, automation, and communications. Furthermore, we have studied the reactive power management in microgrids, a special platform to integrate distributed generations and energy storage in DNs. To solve possible voltage security issues in a microgrid with high penetration of single-phase induction machines under the condition of fault-induced islanding, a voltage-sensitivity-based reactive power management algorithm is proposed

Application of stochastic and evolutionary methods to plan for the installation of energy storage in voltage constrained LV networks

Energy storage is widely considered to be an important component of a decarbonised power system if large amounts of renewable generation are to provide reliable electricity. However, storage is a highly capital intensive asset and clear business cases are needed before storage can be widely deployed. A proposed business case is using storage to prevent overvoltage in low voltage (LV) distribution networks to enable residential photovoltaic systems. Despite storage being widely considered for use in LV networks, there is little work comparing where storage might be installed in LV networks from the perspective of the owners of distribution networks (DNOs). This work addresses this in two ways. Firstly, a tool is developed to examine whether DNOs should support a free market for energy storage in which customers with PV purchase storage (e.g. battery systems) to improve their self-consumption. This reflects a recent policy in Germany. Secondly, a new (published) method is developed which considers how DNOs should purchase and locate storage to prevent overvoltage. Both tools use a snapshot approach by modelling the highest and lowest LV voltages. On their own, these tools enable a DNO to determine the cost of energy storage for a particular LV network with a particular set of loads and with PV installed by a given set of customers. However, in order to predict and understand the future viability of energy storage it is valuable to apply the tools to a large number of LV networks under realistic future scenarios for growth of photovoltaics in the UK power system. Therefore, the work extracts over 9,000 LV network models containing over 40,000 LV feeders from a GIS map of cables provided by one of the UK’s electricity distribution networks- Electricity North West. Applying the proposed tools to these 9,000 network models, the work is able to provide projections for how much LV energy storage would be installed under different scenarios. The cost of doing so is compared to the existing method of preventing reinforcement- LV network reconductoring. This is a novel way of assessing the viability of LV energy storage against traditional approaches and allows the work to draw the following conclusions about the market for energy storage in LV distribution networks in the UK: - Overvoltage as a result of PV could begin to occur in the next few years unless UK regulations for voltage levels are relaxed. There could be a large cost (hundreds of millions of pounds) to prevent this if the traditional approach of reconductoring is used. - If overvoltage begins to occur, a free market for energy storage (randomly purchased by electricity consumers) cannot offer large benefits to DNOs in reducing the reinforcement cost unless this is properly controlled, located and/or widely installed by customers. - Optimally located storage by the DNO can reduce overall reinforcement costs to mitigate overvoltage. This would enable more energy storage to balance renewable generation and present large savings to the power system. The exact topology of storage and the storage rating in each LV network could be determined using the tool proposed in this work

Studies of Economics and Stability with Variable Generation and Controllable Load

This work probes several aspects of the renewable resources and controllable loads. The investigation includes the impact of wind power in bidding process in a deregulated power market, the effect of load damping elements on power system frequency stability and security, and impact of controllable load on system operation from the viewpoint of economic volatility and physical security. In the first part, new bidding models are developed under two schemes for wind generation to analyze the competition among generation companies (GENCOs) with transmission constraints considered. The proposed method employs the supply function equilibrium (SFE) to model a GENCO’s bidding strategy. The bidding process is solved as a bi-level optimization problem. An intelligent search based on Genetic Algorithm (GA) and Monte Carlo simulation (MCS) is applied to obtain the solution. This model also considers the probabilistic variability of wind output. In the second part, the effect of frequency-sensitive load on system frequency using typical system frequency response (SFR) model is investigated. Theoretic analysis based on transfer functions shows that the frequency deviation under a variable load-damping coefficient is relatively small and bounded when the power system is essentially stable; while the frequency deviation can be accelerated when the power system is unstable after disturbance. For the stable case, the largest frequency dip under a perturbation and the corresponding critical time can be derived by inverse Laplace transformation using a full model considering effect of load-damping coefficient. Further, the error in evaluating the load-damping coefficient gives the largest impact on frequency deviation right at the time when the largest frequency dip occurs. In the last part, a new demand response model is presented. It models system economic dispatch as a feedback control process and introduces a flexible and adjustable load cost as a controlled signal to adjust load response. Compared to the conventional “one time use” static load dispatch model, this dynamic feedback demand response model can adjust load to desired level in finite discrete time steps. In addition, MCS and interval mathematics are applied to describing uncertainty of an individual end-user’s response to an ISO’s expected dispatch