Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units\u27 contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany\u27s (GENCO\u27s) profit maximization and thermal units\u27 emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach

Performance Enhancement of Power System Operation and Planning through Advanced Advisory Mechanisms

abstract: This research develops decision support mechanisms for power system operation and planning practices. Contemporary industry practices rely on deterministic approaches to approximate system conditions and handle growing uncertainties from renewable resources. The primary purpose of this research is to identify soft spots of the contemporary industry practices and propose innovative algorithms, methodologies, and tools to improve economics and reliability in power systems. First, this dissertation focuses on transmission thermal constraint relaxation practices. Most system operators employ constraint relaxation practices, which allow certain constraints to be relaxed for penalty prices, in their market models. A proper selection of penalty prices is imperative due to the influence that penalty prices have on generation scheduling and market settlements. However, penalty prices are primarily decided today based on stakeholder negotiations or system operator’s judgments. There is little to no methodology or engineered approach around the determination of these penalty prices. This work proposes new methods that determine the penalty prices for thermal constraint relaxations based on the impact overloading can have on the residual life of the line. This study evaluates the effectiveness of the proposed methods in the short-term operational planning and long-term transmission expansion planning studies. The second part of this dissertation investigates an advanced methodology to handle uncertainties associated with high penetration of renewable resources, which poses new challenges to power system reliability and calls attention to include stochastic modeling within resource scheduling applications. However, the inclusion of stochastic modeling within mathematical programs has been a challenge due to computational complexities. Moreover, market design issues due to the stochastic market environment make it more challenging. Given the importance of reliable and affordable electric power, such a challenge to advance existing deterministic resource scheduling applications is critical. This ongoing and joint research attempts to overcome these hurdles by developing a stochastic look-ahead commitment tool, which is a stand-alone advisory tool. This dissertation contributes to the derivation of a mathematical formulation for the extensive form two-stage stochastic programming model, the utilization of Progressive Hedging decomposition algorithm, and the initial implementation of the Progressive Hedging subproblem along with various heuristic strategies to enhance the computational performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Modified sub-gradient based combined objective technique and evolutionary programming approach for economic dispatch involving valve-point loading, enhanced prohibited zones and ramp rate constraints

A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level shown in Table 6 and displayed in Figure 2 and Figure 3 respectively

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Optimal and Efficient Decision-Making for Power System Expansion Planning

A typical power system consists of three major sectors: generation, transmission, and distribution. Due to ever increasing electricity consumption and aging of the existing components, generation, transmission, and distribution systems and equipment must be analyzed frequently and if needed be replaced and/or expanded timely. By definition, the process of power system expansion planning aims to decide on new as well as upgrading existing system components in order to adequately satisfy the load for a foreseen future. In this dissertation, multiple economically optimal and computationally efficient methods are proposed for expanding power generation, transmission, and distribution systems. First, a computationally efficient model is proposed for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of linear sensitivity factors to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Simulation results show that the proposed model provides the same results as the classical angle-based model while being much faster. Second, a distributed collaborative TEP algorithm for interconnected multi-regional power systems is proposed. The information privacy is respected as each local planner shares limited information related to cross-border tie-lines with its neighboring planners. To coordinate the local planners, a two-level distributed optimization algorithm is proposed based on the concept of analytical target cascading for multidisciplinary design optimization. Third, a security-constrained generation and transmission expansion planning (G&TEP) model with respect to the risk of possible N-1 contingencies is proposed. Using the concept of risk, non-identical probability and severity of individual contingencies are modeled in the proposed G&TEP model. Finally, a mixed-integer linear programming model is proposed for resilient feeder routing in power distribution systems. Geographical information system (GIS) data is used in the proposed model. As it is proven, having GIS facilities will lead to a more cost-efficient and resilient feeder routing scheme than the scheme obtained using electrical points. The proposed model and solution algorithm are comprehensive from several practical aspects such as economic objectives (installation cost, power losses, resiliency), technical constraints (voltage drops, radially constraint, reliability), and geographical constraints (obstacles, right-of-ways)

Enhanced Reserve Procurement Policies for Power Systems with Increasing Penetration Levels of Stochastic Resources

abstract: The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost. Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements. This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Power systems generation scheduling and optimisation using evolutionary computation techniques

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Optimal generation scheduling attempts to minimise the cost of power production while satisfying the various operation constraints and physical limitations on the power system components. The thermal generation scheduling problem can be considered as a power system control problem acting over different time frames. The unit commitment phase determines the optimum pattern for starting up and shutting down the generating units over the designated scheduling period, while the economic dispatch phase is concerned with allocation of the load demand among the on-line generators. In a hydrothermal system the optimal scheduling of generation involves the allocation of generation among the hydro electric and thermal plants so as to minimise total operation costs of thermal plants while satisfying the various constraints on the hydraulic and power system network. This thesis reports on the development of genetic algorithm computation techniques for the solution of the short term generation scheduling problem for power systems having both thermal and hydro units. A comprehensive genetic algorithm modelling framework for thermal and hydrothermal scheduling problems using two genetic algorithm models, a canonical genetic algorithm and a deterministic crowding genetic algorithm, is presented. The thermal scheduling modelling framework incorporates unit minimum up and down times, demand and reserve constraints, cooling time dependent start up costs, unit ramp rates, and multiple unit operating states, while constraints such as multiple cascade hydraulic networks, river transport delays and variable head hydro plants, are accounted for in the hydraulic system modelling. These basic genetic algorithm models have been enhanced, using quasi problem decomposition, and hybridisation techniques, resulting in efficient generation scheduling algorithms. The results of the performance of the algorithms on small, medium and large scale power system problems is presented and compared with other conventional scheduling techniques.Overseas Development Agenc

An Analytical Methodology To Security Constraints Management In Power System Operation

In a deregulated electricity market, Independent System Operators (ISOs) are responsible for dispatching power to the load securely, efficiently, and economically. ISO performs Security Constrained Unit Commitment (SCUC) to guarantee sufficient generation commitment, maximized social welfare and facilitating market-driven economics. A large number of security constraints would render the model impossible to solve under time requirements. Developing a method to identify the minimum set of security constraints without overcommitting is necessary to reduce Mixed Integer Linear Programming (MILP) solution time. To overcome this challenge, we developed a powerful tool called security constraint screening. The proposed approach effectively filters out non-dominating constraints by integrating virtual transactions and capturing changes online in real-time or look-ahead markets. The security-constraint screening takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness is verified using Midcontinent Independent System Operator (MISO) data. The research also presented a data-driven approach to forecast congestion patterns in real-time utilizing machine learning applications. Studies have been conducted using real-world data. The potential benefit is to provide the day-ahead operators with a tool for supporting decision-making regarding modeling constraints

An Enhanced Security-Constrained Unit Commitment Model with Reserve Response Set Policies

Security-constrained unit commitment (SCUC) is a classical problem used for day-ahead commitment, dispatch, and reserve scheduling. Even though SCUC models acquire reserves, N-1 reliability is not guaranteed. This paper presents an enhanced security-constrained unit commitment formulation that facilitates the integration of stochastic resources and accounts for reserve deliverability issues. In this formulation, the SCUC is modified to incorporate a reserve response set model. The enhanced reserve model aims to predict the effects of nodal reserve deployment on critical transmission lines so as to improve the deliverability of reserves post-contingency. The enhanced reserve policies are developed using a knowledge discovery process as a means to predict reserve activation. The approach, thus, aims to acquire reserve at prime locations that face fewer reserve deliverability issues. The results show that the proposed approach consistently outperforms contemporary approaches. All numerical results are based on the IEEE 73-bus test case