Power Economic Dispatch Research Articles

Probabilistic Economic Emission Dispatch Optimization of Multi-sources Power System

The interest on renewable energy resources is growing and the study of different integration aspects of these resources becomes very important to overcome problems caused by their variability or uncertainty. This paper treats the economic environmental power dispatch as a probabilistic multiobjective problem. The operation cost and green house gas emission functions are considered as the sum of deterministic part and probabilistic one. First, the problem is solved based on expected values of generated wind power then, using the cumulative density function (CDF) of each renewable energy source (RES), the CDF of the required reserve to compensate the RESs variability in order to keep the power balance. Then, respecting to the reserve contribution of each thermal generator, the probabilistic part of the global generation cost as well as its CDF are developed. Finally, the proposed approach is applied to solve the active power dispatch problem of IEEE 30-bus test system in two cases with and without RESs. The simulation results show that this method allows to get the complete information about the cumulative distribution function of the actual global cost of the system operation.

Scenario-oriented Design of a Stand-alone Hybrid Power Generation System Using Ammonia/Urine

The paper describes a stand-alone PV/UFC/battery based hybrid power generation system that consists of a PV generator and a urine-fueled fuel cell (UFC) system, and an electricity storage/supply device in parallel to meet the daily load demand. The urine treatment process combined with ammonia cracker is designed to produce high-purity hydrogen. The optimization and simulation of urine-to-H2 processor is investigated. To find a feasible operating scenario, the hybrid power management strategy according to daily load demand, daily solar irradiance, charge/discharge mechanism for battery and external energy burdens is considered as an economic power dispatch problem.

A New Approach ACO for Solving the Compromise Economic and Emission with the Wind Energy

Environmental legislation, with its increasing pressure on the energy sector to control greenhouse gases, is a driving force to reduce CO2 emissions, forced the power system operators to consider the emission problem as a consequential matter beside the economic problems, so the economic power dispatch problem has become a multi-objective optimization problem. This paper sets up an new algorithm, ant colony optimization (ACO), to solve the optimization with combined economic emission dispatch. This problem has been formulated as a multi-objective problem by considering economy, emission and wind energy simultaneously. The feasibility of the proposed approach was tested on 3-unit and 15-unit systems. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques.

Modified random localisation-based DE for static economic power dispatch with generator constraints

In the present study, a modified DE variant called MRLDE is used for solving economic dispatch problem. It is a highly constrained non linear optimisation problem which can be a challenge for traditional gradient based methods. Numerical results and comparison show the efficiency and robustness of the MRLDE algorithm for dealing with such problems.

Comment on “Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction” Shi B, Yan LX, Wu W [Energy 2013; 56: 226–34

This paper comments on the errors of a recently published paper by Shi B, Yan LX, Wu W Energy 2013; 56: 226–34. The Normal Boundary Intersection (NBI)-based optimization technique has been implemented on a test system and the obtained results have been compared to the ones derived from MLCA (multi-objective line-up competition algorithm). The numerical results verify the performance of the proposed technique compared to other method.

Immune-Based Algorithm for Power Economic Emission Dispatch

This paper proposes an artificial immune system for the economic environmental dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The artificial immune system (AIS) equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the constraints of HPS. To show the advantages of the proposed algorithm, one example addressing the best compromise is applied to test the EED problem of the HPS. The proposed algorithm integrates the AIS, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits – 1)ease of implementation; 2)applicability to non-smooth fuel cost and emission level functions; 3)better effectiveness than the previous method, and 4)the requirement for only a small population in applying the optimal EED problem of the HPS.

Artificial Immune Algorithm for Practical Power Economic Dispatch Problems

This paper proposes an artificial immune algorithm with a multiplier updating method (AIA-MU) for practical power economic dispatch (PED) considering units with prohibited operating zones (POZ). The AIA equipped with a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the system constraints. To show the advantages of the proposed algorithm, two examples are investigated, and the computational results of the proposed method are compared with that of the previous methods. The proposed approach integrates the AIA and the MU, revealing that the proposed approach has the following merits - ease of implementation; applicability to non-convex fuel cost functions of the POZ; better effectiveness than previous methods, and the requirement for only a small population in applying the optimal PED problem of generators with POZ.

Economic Dispatch for Power Generation System Incorporating Wind and Photovoltaic Power

With the higher penetrations of wind and photovoltaic power in system, the randomness of its outputs adds amounts of uncertainty to system generation planning and scheduling. This paper formulates a day-ahead power economic dispatch model based on the uncertainty information of wind and photovoltaic power prediction obtained by prediction interval of power with a certain probability. The costs of spinning reserve and interruptible load regarded as spinning reserve are also taken into consideration. The proposed model is solved by Cplex. Finally, the simulation example of system composed of 10-units proves the validity of the model.

Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Cogeneration units which produce both heat and electric power are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units which produce either heat or power exclusively. Hence the economic dispatch problem for these plants optimizing the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the invasive weed optimization algorithm which is inspired by the ecological process of weed colonization and distribution. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over earlier ones.

A new analytical approach for the solution of optimization problem with cubic objective function

A new analytical approach based on dynamic programming method for the solution of cubic objective function with equality and inequality constraints is presented in this paper. A generalized recursive equation is derived to solve the problem. The proposed approach is non-iterative and directly gives the solution without any initial assumption. This method is implemented for economic power dispatch problem to minimize fuel cost and economic dispatch problem of chiller plant for saving energy. The superiority of this method is demonstrated by comparing the experimental results of the proposed method with lambda logic based algorithm and with gradient search method reported in the literature.

Considering the turbine back-pressure effect of thermal units to optimize the PQ_power generation in power system

A new algorithm of PQ_power optimization is mentioned and some typical numerical examples are presented in this article. The fuel cost characteristics being obtained in form of superposition of some high order polynomial and sinusoidal functions can approximately simulate the turbine back-pressure effect of the generator units at the electrical thermal stations and solve the problem of economic active power dispatch. A new loss factor formula expressing the network transmission power losses is a second order polynomial function of generator powers containing a square matrix. This loss factor formula is proposed for optimum solution of generator reactive powers in multi-machine power system.

Hybrid Differential Evolution Harmony Search Algorithm for Power Economic Dispatch Problems

In this paper, a hybrid differential evolution harmony search (HDEHS) algorithm was presented for solving power economic dispatch problems. In this algorithm, mutation and crossover operation instead of harmony memory consideration and pitch adjustment operation, this improved the algorithm convergence rate. Moreover, dynamically adjust the key parameter (e.g. mutagenic factor F, crossover rate CR) to balance the local and global search. Based on a 13 units power system experiment simulations, the HDEHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its three improved algorithms (IHS, GHS and NGHS) that reported in recent literature.

Power system economic dispatch under low‐carbon economy with carbon capture plants considered

Developing a low-carbon power system is critical and fundamental to cope with the challenges of global warming, in which the carbon capture and storage (CCS) technology will play a key role. In this study, the characteristics of energy flow and operation of carbon capture plants (CCPs) are clarified, while the mutual constraint between total generation output of CCPs and operation power consumption of carbon capture system is analysed. Then a generation output model and the optimal dispatch principle of CCPs is established, which can identify how the amount of carbon captured can represent a premium payment that can offset the increase in costs caused by the reduction on power output due to the CCS. On this basis, what with the low-carbon economy factors, a economic power dispatch model under low-carbon economy with CCPs considered is proposed. With the generation fuel cost and carbon emission cost incorporated in the objective function, the model proposed can effectively evaluate the power dispatch problem under low-carbon economy. Studies of the economic power dispatch of the 3-unit, 26-unit and 54-unit test systems show that the model proposed is effective and practical.

Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem

Incremental Artificial Bee Colony algorithm with Local Search (IABC-LS) is one of efficient variant of artificial bee colony optimization which was successfully applied to economic power dispatch problems before. However IABC-LS algorithm has some tunable parameters which are directly affecting the algorithm behavior. In this study, we have introduced a new algorithm namely Artificial Bee Colony with Dynamic Population size (ABCDP) which is using similar mechanisms defined in IABC-LS without using many parameters to be tuned. To prove the efficiency and robustness of algorithm in power dispatch, the algorithm is used for the combined economic and emission dispatch problem which is converted into single objective optimization problem. For fair comparison, the parameters of both IABC and ABCDP algorithms are determined via automatic parameter configuration tool, Iterated F-Race. IEEE 30 bus test system and 40-generator units problem are used as the problem instances. The results of the algorithms indicate that ABCDP is giving good results in both systems and very competitive with the state-of-the-art.

Artificial Bee Colony Optimization Embedded with Simulated Annealing for the Combined Heat and Power Economic Dispatch Problem

This paper presents a new approach for solving the Combined Heat and Economic Dispatch (CHPED) problem using an integrated algorithm based on artificial bee colony algorithm (ABC) and Simulated Annealing (SA). Artificial Bee Colony algorithm (ABC) is inspired by the foraging behavior of honey bee swarm, is a biological inspired optimization. It shows more effective than the other optimization algorithms. However, ABC is good at exploration but poor at exploitation, and its convergence speed is also slow in some cases. To overcome this deficiency, this paper proposes an improved ABC algorithm called ABC-SA algorithm. In this algorithm ABC is acting as a base level search to direct the search towards the optima region and local searches synergistically combined with simulated Annealing (SA). The performance of the proposed algorithm (ABC-SA) is validated by illustration with test system. The results of the proposed algorithm are compared with those of Practical Swarm Optimization (PSO), ABC, Real –Coded Genetic Algorithm (RCGA), Bee Colony Optimization (BCO), SA and Evolutionary Programming techniques (EP). From numerical results, it is seen that the proposed algorithm is able to provide a better solution at a lesser computational effort.

A harmony-genetic based heuristic approach toward economic dispatching combined heat and power

The Combined Heat and Power Economic Dispatch (CHPED) problem seeks to determine the heat and power production to minimize the system production costs and satisfy the heat–power demands and capacity constraints. This study examines the combined heat and power dispatching needs of cogeneration plants, and investigates the performance of an evolutionary computing approach which is based on both genetic algorithm (GA) and harmony search (HS). Experimental results were conducted for an extensive comparison with GA and HS to confirm the superior performance of this hybrid approach in cost minimization and computation times. The output results indicate that the proposed algorithm is capable of managing the CHPED problem and yields high-quality solutions.

Multi-objective optimization for combined heat and power economic dispatch with power transmission loss and emission reduction

The growing concern over environmental considerations and the increasing fuel cost call for operating the energy station in a more economical and environmentally friendly way. This paper develops a multi-objective model for the CHPED (combined heat and power economic dispatch) problem to conventional energy stations, where the competing fuel cost and environmental impact objectives are simultaneously optimized. To model the problem accurately, both non-linear fuel cost functions with valve-point loading effects and power transmission loss are explicitly considered in this model. A novel MLCA (multi-objective line-up competition algorithm) is proposed to handle the problem as a nonlinear constrained multi-objective optimization problem. Some diversity-preserving mechanisms are employed in the MLCA to produce well-distributed Pareto-optimal solutions. Moreover, a fuzzy decision-making process is applied to extract the best compromise nondominated solution from the Pareto-optimal set. The performance of the proposed method is validated by two typical systems. Comparison results have demonstrated the superiority of the proposed approach and confirmed its potential to solve the larger multi-objective CHPED problem.

Combined heat and power economic emission dispatch using nondominated sorting genetic algorithm-II

This paper presents nondominated sorting genetic algorithm-II for solving combined heat and power economic emission dispatch problem. The problem is formulated as a nonlinear constrained multi-objective optimization problem. Nondominated sorting genetic algorithm-II is proposed to handle economic emission dispatch as a true multi-objective optimization problem with competing and noncommensurable objectives. The proposed algorithm is illustrated for two test systems and the test results are compared with those obtained from strength pareto evolutionary algorithm 2.

Combined heat and power economic dispatch problem using firefly algorithm

Cogeneration units, which produce both heat and electric power, are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units, which produce either heat or power exclusively. Hence, the economic dispatch problem for these plants to optimize the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the firefly algorithm, which is inspired by the behavior of fireflies which attract each other based on their luminosity. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over the earlier methods.

A demonstration of the improved efficiency of the canonical coordinates method using nonlinear combined heat and power economic dispatch problems

Economic dispatch is the short-term determination of the optimal output from a number of electricity generation facilities to meet the system load while providing power. As such, it represents one of the main optimization problems in the operation of electrical power systems. This article presents techniques to substantially improve the efficiency of the canonical coordinates method (CCM) algorithm when applied to nonlinear combined heat and power economic dispatch (CHPED) problems. The improvement is to eliminate the need to solve a system of nonlinear differential equations, which appears in the line search process in the CCM algorithm. The modified algorithm was tested and the analytical solution was verified using nonlinear CHPED optimization problems, thereby demonstrating the effectiveness of the algorithm. The CCM methods proved numerically stable and, in the case of nonlinear programs, produced solutions with unprecedented accuracy within a reasonable time.