Constraints Of Generating Units Research Articles

Non-convex constrained economic power dispatch with prohibited operating zones and piecewise quadratic cost functions

This paper is focused on the solution of the non-convex economic power dispatch problem with piecewise quadratic cost functions and practical operation constraints of generation units. The constraints of the economic dispatch problem are power balance constraint, generation limits constraint, prohibited operating zones and transmission power losses. To solve this problem, a meta-heuristic optimization algorithm named crow search algorithm is proposed. A constraint handling technique is also implemented to satisfy the constraints effectively. For the verification of the effectiveness and the superiority of the proposed algorithm, it is tested on 6-unit, 10-unit and 15-unit test systems. The simulation results and statistical analysis show the efficiency of the proposed algorithm. Also, the results confirm the superiority and the high-quality solutions of the proposed algorithm when compared to the other reported algorithms.

Distributed Prescribed Finite Time Consensus Scheme for Economic Dispatch of Smart Grids with the Valve Point Effect

The distributed prescribed finite time consensus schemes for economic dispatch (ED) of smart grids with and without the valve point effect are researched in this paper. First, the optimization problem is transformed into a consensus of multiagent system problem, where both with and without the valve point effect are considered. Second, for the directed balance network, a prescribed finite time method has been arranged to solve the ED problem with and without the valve point effect. Third, with considering the constraints of generation units, the prescribed finite time result is also achieved. Finally, from the simulations, the efficiency of the proposed algorithms is validated.

Mixed-integer nonseparable piecewise linear models for the hydropower production function in the Unit Commitment problem

The hydro unit commitment (HUC) problem seeks to determine, for a short-term horizon with (semi)hourly discretization, the status (on/off) and generation level of each generating unit (GU) to meet plant and GUs constraints. In the HUC problem, the nonlinearities and non-convexities of the hydro production function (HPF), and the presence of binary variables that identify which GUs must be dispatched at each time step make the search for a solution challenging. Due to the recent developments in commercial mixed-integer linear programming (MILP) solvers, it is possible to approximate the nonlinear and nonconvex HPF through piecewise-linear (PWL) models with reasonable accuracy. Throughout this paper we emphasize the potential advantages of seven MILP formulations that can be categorized as parametric and non-parametric methods. Given the complexities of state-of-the-art solvers, it is hard to predict which formulation performs better. Although some guidelines can be found in literature, the formulation that performs best can be strongly dependent on the specific problem structure or data. In this context, we develop and compare seven multidimensional nonseparable PWL models for representing the HPF in the HUC problem. To assess the performance of each PWL model, we present results using a 6-GU hydro plant of two different types.

Internet of Energy (IoE) and High-Renewables Electricity System Market Design

The growing importance of the Internet of Energy (IoE) brands the high-renewables electricity system a realistic scenario for the future electricity system market design. In general, the whole gist behind the IoE is developed upon a somewhat broader idea encompassing the so-called “Internet of Things” (IoT), which envisioned a plethora of household appliances, utensils, clothing, smart trackers, smart meters, and vehicles furnished with tiny devices. These devices would record all possible data from all those objects in real time and allow for a two-way exchange of information that makes it possible to optimize their use. IoT employs the Internet Protocol (IP) and the worldwide web (WWW) network for transferring information and data through various types of networks and gateways as well as sensor technologies. This paper presents an outline stemming from the implications of the high-renewables electric system that would employ the Internet of Energy (IoE). In doing so, it focuses on the implications that IoE brings into the high-renewables electricity market inhabited by smart homes, smart meters, electric vehicles, solar panels, and wind turbines, such as the peer-to-peer (P2P) energy exchange between prosumers, optimization of location of charging stations for electric vehicles (EVs), or the information and energy exchange in the smart grids. We show that such issues as compatibility, connection speed, and most notoriously, trust in IoE applications among households and consumers would play a decisive role in the transition to the high-renewables electricity systems of the 21st century. Our findings demonstrate that the decentralized approach to energy system effective control and operation that is offered by IoE is highly likely to become ubiquitous as early as 2030. Since it may be optimal that large-scale rollouts start in the early 2020s, some form of government incentives and funding (e.g. subsidies for installing wind turbines or solar panels or special feed-in-tariffs for buying renewable energy) may be needed for the energy market to make early progress in embracing more renewables and in reducing the costs of later investments. In addition, there might be some other alternative approaches aimed at facilitating this development. We show that the objective is to minimize the overall system cost, which consists of the system investment cost and the system operating cost, subject to CO2 emissions constraints and the operating constraints of generation units, network assets, and novel carbon-free technologies, which is quite cumbersome given the trend in consumption and the planned obsolescence. This can be done through increasing energy efficiency, developing demand side management strategies, and improving matching between supply and demand side, just to name a few possibilities.

Stochastic Network-Constrained Unit Commitment to Determine Flexible Ramp Reserve for Handling Wind Power and Demand Uncertainties

The flexible ramp reserve of dispatchable generators is a key effective means to compensate the fast fluctuation of wind power generators and the forecast error of demand. This article proposes a stochastic network-constrained unit commitment (NCUC) model to determine, on daily horizon, the optimal schedule of generation units along with the required flexible ramp and spinning reserves under uncertainties of wind power and demand. Point estimate method (PEM) is utilized to reduce the number of scenarios that model wind power and demand uncertainties. In addition to PEM scenarios, some worst-case scenarios based on regional wind and demand variations are defined and incorporated in the proposed model. The NCUC model includes for these scenarios various technical constraints of generation units and operation constraints of the network. To ensure computational tractability of NCUC model, the ac network operation constraints are linearized. The proposed mixed integer linear programming NCUC model is tested using the IEEE 118-bus system. The obtained results show better units scheduling than the conventional (deterministic) NCUC model.

A Dynamic Multistage Stochastic Unit Commitment Formulation for Intraday Markets

As net-load becomes less predictable there is a lot of pressure in changing decision models for power markets such that they account explicitly for future scenarios in making commitment decisions. This paper proposes to make commitment decisions with multiple gate closures. Our proposed model also leverages a state-space formulation for the commitment variables, through which the operational constraints of generation units participating in the market are respected. We also study the problem of constructing scenario tree approximations for stochastic processes and evaluate our algorithms on scenario tree libraries derived from real net-load data.

A parametric approach for the valuation of power plant flexibility options

Conventional generation units encounter a changing role in modern societies’ energy supply. With increased need for flexible operation, engineers and project managers have to evaluate the benefits of technical improvements. For this purpose, a valuation tool has been developed, comparing economical cornerstones and technical constraints of generation units to European Energy Exchange prices for PHELIX 2014. It enables the user to relate a change in technical parameters to an economic effect and possible revenues. Four different types of conventional power plants are investigated in scenarios with increasing CO2 and fuel prices to determine the impact of different flexibility options. Results show that an increased ramp rate has not the same magnitude of positive economic impact as reduced minimum operation load, based on an observation on a price signal with resolution of fifteen minutes.

An Improved TLBO Algorithm to Solve Profit Based Unit Commitment Problem under Deregulated Environment

The TLBO (Teaching learning based optimization technique) is one of the recently developed algorithm and In this paper, an improved TLBO algorithm is proposed to address the profit based unit commitment problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi constrained non linear optimization problem. The PBUC involves determining the on/off states of the generating units while satisfying demand and generating unit constraints. Some of the recently developed algorithms like GA, PSO, BBO, ACO etc. are available to solve this complex problem, but so far there is no such ideal technique which can completely address this problem. In this scenario an attempt is made to solve this problem using an improved TLBO. The regular TLBO algorithm is improved by considering some of the factors like teaching factor, number of teachers, learning through tutorials, self motivated learning. The step-by-step procedure of how to apply the improved TLBO algorithm for PBUC problem is presented. The proposed algorithm is tested on IEEE 10 unit 10hours load demand as input data for simulation using MATLAB R2009a version. From the results obtained it is observed that the improved TLBO algorithm is effective compared to traditional TLBO while handling the computation time and the dimension of the problem.

Short-term scheduling of combined heat and power generation units in the presence of demand response programs

This paper presents the short-term hourly scheduling of industrial and commercial customers with cogeneration facilities, conventional power units, and heat-only units. In order to serve the power and heat demands of the customer with minimum cost, demand response (DR) program has been implemented. In the proposed DR program total power and heat demand of customer will be supplied, without any curtailed load. Moreover, the responsive load can vary in different time intervals. In the paper, the heat-power dual dependency characteristic in different types of CHP (combined heat and power generation) units is taken into account and all technical constraints of generation units have been satisfied. In addition, a heat buffer tank, with the ability of heat storage, has been incorporated in the proposed framework. This work studies four cases in order to confirm the performance of the proposed method. The importance of applying the proposed DR program, the effect of considering the amount of transferable power constraint and the effect of heat exchange with the nearby factories in the value of expected profit have been investigated in the case studies.

Binary real coded firefly algorithm for solving unit commitment problem

This paper presents a new biologically-inspired binary real coded firefly (BRCFF) algorithm to solve the unit commitment problem (UCP) by considering system and generating unit constraints. The firefly (FF) algorithm is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication. Upon solving UCP, the proposed binary coded FF algorithm determines the ON/OFF status of the generating units, while the economic dispatch problem (EDP) is solved using the real coded FF algorithm. The manner of firefly communication through luminescent flashes and their synchronization is imitated and suitably implemented in UCP. An effective constraint handling mechanism is introduced to solve complicated system and unit constraints. Finally, the proposed algorithm is applied to 3, 12, 17, 26, and 38 generating unit systems for a 24h scheduling horizon and a comparative study is conducted using other recently reported results. Numerical results clarify and verify the significance of the proposed algorithm. The results obtained indicate that the proposed biologically-inspired algorithm could be an important player in swarm-based optimization.

Security-Constrained Generation and Transmission Outage Scheduling With Uncertainties

This study presents a stochastic model for the independent system operator's (ISO's) optimal coordinated long-term maintenance scheduling of generation units and transmission lines with short-term security-constrained unit commitment (SCUC). Random disturbances of power systems including forced outages of generation units and transmission lines, load forecast errors, and fuel price fluctuations are modeled as scenario trees using the Monte Carlo simulation. Lagrangian relaxation (LR) is applied to separate the coordinated optimization problem into long-term equipment maintenance (LTEM) and stochastic long-term SCUC (LTSCUC) subproblems. For the stochastic LTSCUC subproblem, scenario bundle constraints are relaxed via LR and the optimization problem is decomposed into deterministic LTSCUC problems. LR is applied to each deterministic LTSCUC to relax long-term fuel and emission limits and decompose the problem into short-term SCUC subproblems. The decomposition is further applied to short-term SCUC subproblems for separating hourly unit commitment and transmission network constraints. The unit commitment is formulated as a mixed-integer programming (MIP) problem and solved by the branch-and-cut method using CPLEX. The outcome of this study includes the hourly scheduling of outages of generation units and transmission lines, which corresponds to the optimal generation unit commitment and dispatch, and transmission flows. The hourly schedules minimize the total cost of operation and maintenance and satisfy long-term and short-term constraints of generation units and transmission network with the inclusion of power system uncertainty. A modified IEEE 118-bus system is used to exhibit the effectiveness of the proposed scheduling approach.

Daily Operational Planning of the EDF Plant Mix Proposal for a New Method

Lagrangian relaxation in the service of the operational planning of an electric power plant mix How can the daily operational planning of an electric power plant mix using thermal and hydraulic equipment be optimized ? The approach adopted in this paper makes it possible to take into account thoroughly the operating constraints of generation units and those of valleys. The approach provides a quasi-optimum solution to the problem. In addition to the generation schedules, it determines the daily plant discharge and the trade-off between thermal and hydraulic spinning reserves. The method used is that of the decomposition-coordination by prices, or Lagrangian relaxation. The global problem is broken down into two classes of local subproblems: - optimum planning of each thermal unit using dynamic programming, - optimum planning of each hydraulic valley using linear programming. All the local subproblems are coordinated among themselves by the costs of energy and the spinning reserve, which are dual variables associated with the corresponding constraints.