Ramp Rate Limits Research Articles

Proposed Differential Evolution Technique for Solving the Power System Economic Load Dispatch Problem

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. In This paper presents an application of the Differential Evolution optimization algorithm to power system economic load dispatch problem with ramp rate limit for 15 unit test case system. Power system Economic load dispatch problems are applied and compared its solution quality and computation efficiency to Genetic algorithm (GA) and Differential Evolution optimization algorithm.

Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables—Part II: Case-Study

The novel cascade-mitigation scheme developed in Part I of this paper is implemented within a receding-horizon model predictive control (MPC) scheme with a linear controller model. This present paper illustrates the MPC strategy with a case-study that is based on the IEEE RTS-96 network, though with energy storage and renewable generation added. It is shown that the MPC strategy alleviates temperature overloads on transmission lines by rescheduling generation, energy storage, and other network elements, while taking into account ramp-rate limits and network limitations. Resilient performance is achieved despite the use of a simplified linear controller model. The MPC scheme is compared against a base-case that seeks to emulate human operator behavior.

Model-Predictive Cascade Mitigation in Electric Power Systems With Storage and Renewables—Part I: Theory and Implementation

A novel model predictive control (MPC) scheme is developed for mitigating the effects of severe line-overload disturbances in electrical power systems. A piece-wise linear convex approximation of line losses is employed to model the effect of transmission line power flow on conductor temperatures. Control is achieved through a receding-horizon model predictive control (MPC) strategy which alleviates line temperature overloads and thereby prevents the propagation of outages. The MPC strategy adjusts line flows by rescheduling generation, energy storage and controllable load, while taking into account ramp-rate limits and network limitations. In Part II of this paper, the MPC strategy is illustrated through simulation of the IEEE RTS-96 network, augmented to incorporate energy storage and renewable generation.

Non-convex emission constrained economic dispatch using a new self-adaptive particle swarm optimization technique

This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses.

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

An Improved Particle Swarm Optimization for the Combined Heat and Power Dynamic Economic Dispatch Problem

—This study presents a novel improved particle swarm optimization algorithm to solve the combined heat and power dynamic economic dispatch problem. This problem is formulated as a challenging non-convex and non-linear optimization problem considering practical characteristics, such as valve-point effects, transmission losses, ramp-rate limits, mutual dependency of power and heat, spinning reserve requirements, and transmission security constraints. The proposed method combines classical particle swarm optimization with a chaotic mechanism, time-variant acceleration coefficients, and a self-adaptive mutation scheme to prevent premature convergence and improve solution quality. Moreover, multiple efficient constraint handling strategies are employed to deal with complex constraints. The effectiveness of the proposed improved particle swarm optimization for solving the combined heat and power dynamic economic dispatch problem is validated on three different test systems, and the results are compared with those of other variants of particle swarm optimization as well as other methods reported in the literature. The numerical results demonstrate the superiority of improved particle swarm optimization in solving the combined heat and power dynamic economic dispatch problem while strictly satisfying all the constraints.

Comparative analysis of optimal load dispatch through evolutionary algorithms

This paper presents an evolutionary algorithm named as Cuckoo Search algorithm applied to non-convex economic load dispatch problems. Economic load dispatch (ELD) is very essential for allocating optimally generated power to the committed generators in the system by satisfying all of the constraints. Various evolutionary techniques like Genetic Algorithm (GA), Evolutionary programming, Particle Swarm Optimization (PSO) and Cuckoo Search algorithm are considered to solve dispatch problems. To verify the robustness of the proposed Cuckoo Search based algorithm, constraints like valve point loading, ramp rate limits, prohibited operating zones, multiple fuel options, generation limits and losses are also incorporated in the system. In the Cuckoo Search algorithm, the levy flights and the behavior of alien egg discovery is used to search the optimal solution. In comparison with the solution quality and execution time obtained by five test systems, the proposed algorithm seems to be a promising technique to solve realistic dispatch problems.

Real-Time Wavelet-Based Coordinated Control of Hybrid Energy Storage Systems for Denoising and Flattening Wind Power Output

Since the penetration level of wind energy is continuously increasing, the negative impact caused by the fluctuation of wind power output needs to be carefully managed. This paper proposes a novel real-time coordinated control algorithm based on a wavelet transform to mitigate both short-term and long-term fluctuations by using a hybrid energy storage system (HESS). The short-term fluctuation is eliminated by using an electric double-layer capacitor (EDLC), while the wind-HESS system output is kept constant during each 10-min period by a Ni-MH battery (NB). State-of-charge (SOC) control strategies for both EDLC and NB are proposed to maintain the SOC level of storage within safe operating limits. A ramp rate limitation (RRL) requirement is also considered in the proposed algorithm. The effectiveness of the proposed algorithm has been tested by using real time simulation. The simulation model of the wind-HESS system is developed in the real-time digital simulator (RTDS)/RSCAD environment. The proposed algorithm is also implemented as a user defined model of the RSCAD. The simulation results demonstrate that the HESS with the proposed control algorithm can indeed assist in dealing with the variation of wind power generation. Moreover, the proposed method shows better performance in smoothing out the fluctuation and managing the SOC of battery and EDLC than the simple moving average (SMA) based method.

Optimal economic dispatch with valve loading effect using self-adaptive firefly algorithm

Economic dispatch (ED) problem exhibits highly nonlinear characteristics, such as prohibited operating zone, ramp rate limits, and non-smooth property. Due to its nonlinear characteristics, it is hard to achieve the expected solution by the classical methods. To overcome the challenging difficulty, this paper proposes an improved firefly algorithm (FA) to solve economic dispatch (ED) problem. The improved FA employs two strategies to enhance the search ability and avoid the premature usually suffered from in standard FA. The first one is based on the distance information among the fireflies, and it adjusts the light absorption coefficient adaptively. The other one is a decreasing strategy for the randomization parameter. Additionally, a crossover operation is employed to create potential solution with high diversity. The designs are able to enhance the search ability and performance of FA, which have been demonstrated on six benchmark functions. To validate the proposed algorithm, we also use three different systems to demonstrate its efficiency and feasibility in solving ED problem. The experimental results show that the proposed FA method was capable of achieving higher quality solutions in ED problems.

An ultra-capacitor for frequency stability enhancement in small-isolated power systems: Models, simulation and field tests

The most relevant issue in operation of isolated power systems is frequency stability. Frequency stability is concerned with the ability of generators to supply the loads at an acceptable frequency after a disturbance. Frequency stability is governed by the kinetic energy stored in the generator-prime mover rotating masses and the prime mover frequency primary regulation. If frequency excursions are not within +/−2.5Hz range, cascade tripping of the remaining generators can occur because of generator over/under frequency protections tripping. Energy storage systems can contribute to frequency stability enhancement if their discharging is governed by a frequency controller.Endesa is leading a research project on testing the state of the art of energy storage systems for several applications (peak-shaving, voltage control, frequency control) in several isolated power systems of the Canary Islands. Several applications are being investigated. One of them consists on the application of a 4MW–5s ultracapacitor (UC) for frequency stability enhancement of the La Palma power system.This paper reports the dynamic model developed for time domain simulation and controller design of frequency stability, and field tests undertaken to validate models and the controller settings. A simple but still accurate model is presented. The proposed model takes into account the UC’s state of charge (SoC) and it represents the dynamics of the power electronics by means of a non-linear first-order model. The frequency control consists of droop control and inertia emulation. Ramp rate limits, power limits and SoC are also taken into account in the frequency control. In comparison with the recorded field tests, the proposed model is able to accurately represent the response of the UC for the purpose of frequency stability analysis.

Artificial Bee Colony Optimization for Short-Term Hydrothermal Scheduling

Artificial bee colony optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. Artificial bee colony optimization is a swarm-based algorithm inspired by the food foraging behavior of honey bees. The algorithm is tested on a multi-reservoir cascaded hydroelectric system having prohibited operating zones and thermal units with valve point loading. The ramp-rate limits of thermal generators are taken into consideration. The transmission losses are also accounted for through the use of loss coefficients. The algorithm is tested on two hydrothermal multi-reservoir cascaded hydroelectric test systems. The results of the proposed approach are compared with those of differential evolution, evolutionary programming and particle swarm optimization. From numerical results, it is found that the proposed artificial bee colony optimization based approach is able to provide better solution.

Solution to Electric Power Dispatch Problem Using Fuzzy Particle Swarm Optimization Algorithm

This paper presents the application of fuzzy particle swarm optimization to constrained economic load dispatch (ELD) problem of thermal units. Several factors such as quadratic cost functions with valve point loading, ramp rate limits and prohibited operating zone are considered in the computation models. The Fuzzy particle swarm optimization (FPSO) provides a new mechanism to avoid premature convergence problem. The performance of proposed algorithm is evaluated on four test systems. Results obtained by proposed method have been compared with those obtained by PSO method and literature results. The experimental results show that proposed FPSO method is capable of obtaining minimum fuel costs in fewer numbers of iterations.

The effect of variability-mitigating market rules on the operation of wind power plants

In power systems with many wind generators, market rules have been slowly changing in order to mitigate or internalize the system costs relating to wind variability. We examine several potential market policies for mitigating the effects of wind variability. For each market policy, we determine the effect that the policy would have on the operation and profitability of wind plants, using time series analysis to estimate the actions of profit-maximizing wind generators acting as price takers. We identify policy scenarios that significantly reduce short-term (30-min) wind fluctuations while having little effect on wind plant revenue. For example, in a scenario where the ramp-rate of wind is limited and ramping violations are assigned a small monetary penalty, a loose ramp rate limit (40 % per 15-min period) can reduce 30-min fluctuations by 35 % while reducing wind plant revenue by only 0.2 %. The novel market-based strategies investigated in this work may enable reductions in the overall system cost of wind integration and can be designed so that both wind generators and system operators are better off than under the status quo.

A hybrid harmony search with arithmetic crossover operation for economic dispatch

Economic dispatch (ED) problems often exhibit non-linear, non-convex characteristics due to the valve point effects. Further, various constraints and factors, such as prohibited operation zones, ramp rate limits and security constraints imposed by the generating units, and power loss in transmission make it even more challenging to obtain the global optimum using conventional mathematical methods. Meta-heuristic approaches are capable of solving non-linear, non-continuous and non-convex problems effectively as they impose no requirements on the optimization problems. However, most methods reported so far mainly focus on a specific type of ED problems, such as static or dynamic ED problems. This paper proposes a hybrid harmony search with arithmetic crossover operation, namely ACHS, for solving five different types of ED problems, including static ED with valve point effects, ED with prohibited operating zones, ED considering multiple fuel cells, combined heat and power ED, and dynamic ED. In this proposed ACHS, the global best information and arithmetic crossover are used to update the newly generated solution and speed up the convergence, which contributes to the algorithm exploitation capability. To balance the exploitation and exploration capabilities, the opposition based learning (OBL) strategy is employed to enhance the diversity of solutions. Further, four commonly used crossover operators are also investigated, and the arithmetic crossover shows its efficiency than the others when they are incorporated into HS. To make a comprehensive study on its scalability, ACHS is first tested on a group of benchmark functions with a 100 dimensions and compared with several state-of-the-art methods. Then it is used to solve seven different ED cases and compared with the results reported in literatures. All the results confirm the superiority of the ACHS for different optimization problems.

Chemical reaction optimisation for different economic dispatch problems

This study presents a real coded chemical reaction algorithm to solve economic load dispatch (ELD) problems involving different constraints such as power balance, ramp rate limits and prohibited operating zone constraints. Effects of valve-point loading and multi-fuel options of large-scale thermal plants are also studied. System transmission loss has also been considered in a few cases. Chemical reaction optimisation mimics the interactions of molecules in a chemical reaction to reach from a higher energy unstable state to a low energy stable state. A real coded version, known as real-coded chemical reaction optimisation is implemented here to solve ELD problems. The simulation results establish that the proposed approach outperforms several other existing optimisation techniques in terms of quality of solution obtained and computational efficiency. The results also prove the robustness of the proposed methodology to solve ELD problems.

Fast Dynamic Economic Power Dispatch Problems Solution Via Optimality Condition Decomposition

This letter proposes an efficient real-time approach based on optimality condition decomposition (OCD) technique to solve dynamic economic dispatch (DED) problem. Ramp-rate limits, prohibited operation zones (POZs) constraints, along with transmission losses are considered in the studied DED model. In order to examine the effectiveness of the proposed approach, it is implemented on a 54-unit test system. The obtained results verify applicability of the proposed method for solving the DED problem in real-time environment.

Fast $\lambda$-Iteration Method for Economic Dispatch With Prohibited Operating Zones

This letter presents a new method, the fast λ-iteration (FλI) method, to solve the economic dispatch (ED) problem considering the prohibited operating zones (POZs) and ramp rate limits of generation units. Necessary conditions for the optimal solution of the ED problem are presented and proved. The efficiency of the method has been verified on a 15-unit system and a Korea 140-unit system.

Solving economic load dispatch problems using chaotic self-adaptive differential harmony search algorithm

Summary In this paper, two improved differential harmony search algorithms called self-adaptive differential harmony search (SADHS) algorithm and chaotic SADHS (CSADHS) algorithm are proposed to solve economic load dispatch problems with valve-point effect, ramp-rate limits, prohibited operating zones, and multiple fuel options. The effectiveness and suitability of the proposed algorithms are demonstrated on test systems consisting of 13, 15, and 40 generating units with valve-point effect and 10 generating units with multiple fuel options and valve-point effect. The simulation results obtained by the proposed algorithms are compared with the results obtained using DHS and with the results of other methods reported in the literature. The solution obtained by the proposed CSADHS algorithm is found to be better than the other methods in terms of solution quality. In terms of convergence speed and computation time, the proposed CSADHS algorithm is better than DHS and the other proposed SADHS algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Economic Load Dispatch Downside with Valve - Point Result Employing a Binary Bat Formula

This paper proposes application of BAT algorithm for solving economic load dispatch problem. BAT algorithmic rule is predicated on the localization characteristics of micro bats. The proposed approach has been examined and tested with the numerical results of economic load dispatch problems with three and five generating units with valve point loading without considering prohibited operating zones and ramp rate limits. The results of the projected BAT formula are compared with that of other techniques such as lambda iteration, GA, PSO, APSO, ABC and basic principle. For each the cases, the projected algorithmic program outperforms the answer reported for the existing algorithms. Additionally, the promising results show the hardiness, quick convergence and potency of the projected technique. DOI: http://dx.doi.org/10.11591/ijece.v4i1.4233

Solving the Power Economic Dispatch Problem With Generator Constraints by Random Drift Particle Swarm Optimization

This paper proposes the random drift particle swarm optimization (RDPSO) algorithm to solve economic dispatch (ED) problems from power systems area. The RDPSO is inspired by the free electron model in metal conductors placed in an external electric field, and it employs a novel set of evolution equations that can enhance the global search ability of the algorithm. Many nonlinear characteristics of a power generator, such as the ramp rate limits, prohibited operating zones and nonsmooth cost functions are considered when the proposed method is used in practice for optimizing the generators' operation. The performance of the RDPSO method is evaluated on three different power systems, and compared with that of other optimization methods in terms of the solution quality, robustness, and convergence performance. The experimental results show that the RDPSO method performs better in solving the ED problems than any other tested optimization techniques.