Generating Units In Power System Research Articles

Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Impact of Large-scale concentrated solar power on energy and auxiliary markets

Concentrated solar power (CSP) generation has been widely regarded as a promising technology to increase the penetration of renewable energy-based generation units in power systems. Numerous research studies have been conducted in various ways to promote the participation of a CSP generator as a price-taker in electricity markets. However, it remains an open question as to how the strategic behavior of a CSP generator as a price-maker affects electricity markets. To this end, we conducted a numerical analysis to quantify the impact of a strategic large-scale CSP generator on energy and auxiliary markets. A three-stage stochastic bi-level model is proposed to describe the strategic behavior of a CSP generator as a price-maker in electricity markets. The impact of the risk-averse level of a CSP generator is also incorporated. Moreover, network constraints are considered to make our conclusion more realistic. The proposed bi-level model is recast into a mathematical programming with equilibrium constraints form firstly by using Karush-Kuhn-Tucker conditions and then a mixed-integer-liner-programming form by using the strong duality theory, and big-M method. Case studies based on an IEEE-56 bus system demonstrate that a price-maker CSP generator can decrease the price of both energy markets and reserve markets. However, an associated thermal energy storage tank can alleviate the negative impact of a CSP generator on electricity markets. Additionally, the high risk-averse level of a CSP generator will lead to an increased equilibrium price.

A Discrete Chaotic Jaya algorithm for optimal preventive maintenance scheduling of power systems generators

The main role of the optimal Generator Maintenance Scheduling (GMS) problem in power systems is to develop an optimal preventive maintenance scheduling of the generation part units. An optimal GMS provides power systems with higher operational reliability, extends the generators lifetime and reduces the cost of generators maintenance. The GMS problem is formulated as an optimisation problem. This problem should satisfy both load’s power demand and workforce constraints with ensuring the reliability of power systems at economical operation cost. The GMS problem has been studied for many years when exact mathematical methods have been used in the past to reach exact solutions for small-scale problems. However, these conventional mathematical approaches have many limitations and they suffer from unreasonable computational efforts as system dimension increases. Traditional approximate methods have been adopted to overcome the limitations of exact methods for medium-scale power systems. However, they provide approximate solutions and they require a large computational effort for wide-area systems of big dimensions. Recently, modern methods based on metaheuristics optimisation have taken a long part to solve the GMS problem and to overcome the limitations of approximate methods. In this paper, a proposed Discrete Chaotic Jaya Optimisation (DCJO) algorithm is employed to perform the preventive maintenance scheduling of electric power systems generators. The proposed algorithm is based on a cooperation between the discrete Jaya optimisation algorithm and a proposed move rule based on Chaotic Local Search (CLS) technique to improve both exploration and exploitation phases. The GMS problem is modelled based on the reliability criterion of an objective function of a sum of the squares of the reserves of generation. The optimisation process is performed through minimising an evaluation function of a weighted sum of the objective function and the penalty function for violations of the constraints. The proposed approach has been tested in a 21-unit test system over a planned horizon of 52 weeks in which the peak load is 4739 MW and the maximum generation is 5688 MW and there is a total number of 35 workforce available per week to perform the maintenance tasks. The proposed method has been compared through several statistical tests with recent algorithms of the related works. The obtained results show the effectiveness of the proposed algorithm for solving the GMS problem as compared to other recent algorithms. This approach can be relied at the present upon to solve maintenance scheduling problems of generation units in power systems.

Optimum Placement of Distribution Generation Units in Power System with Fault Current Limiters Using Improved Coyote Optimization Algorithm.

Electric power frameworks become intensely loaded because of the expanded power demand, and as a result, the power system faces great power losses and fault currents. The integration of Distribution Generation (DG) units plays a key role in minimizing the load pressure on a power system. DGs are transmitted with a high fault current, which surpasses the evaluations of circuit breakers. This paper presents various DG units’ optimal placement with Fault Current Limiters (FCLs) in different phases. The Improved Coyote Optimize Algorithm (ICOA) and Electrical Transient Analyzer Program (ETAP) are assessed for the proposed technique in terms of normal and faulty working status. Similarly, to enhance the efficiency of a distribution system, a fuzzy-based multi-objective mechanism is applied. The proposed method is employed on an IEEE 21-bus and 28-bus distribution system. The simulation analysis proved that the power losses and fault levels are reduced at an acceptable level.

Multi-Objective Tabu Search for the Location and Sizing of Multiple Types of FACTS and DG in Electrical Networks

Flexible AC transmission systems and distributed generation units in power systems provide several benefits such as voltage stability, power loss minimization, thermal limits enhancement, or enables power system management close to the limit operation points; and by extension, economic benefits such as power fuel cost and power loss cost minimization. This work presents a multi-objective optimization algorithm to determine the location and size of hybrid solutions based on a combination of Flexible AC transmission systems devices and distributed generation. Further, the work expands the types of FACTS usually considered. The problem is solved by means of a Tabu search algorithm with good results when tested in a network of 300 nodes.

A multi-objective framework for multi-area economic emission dispatch

Economic dispatch seeks for the most economical combination of generation units in power system while satisfying bunch of physical and operational constraints. Finding the most economical generation strategy in power markets with several generation zones is crucial for power system operation. Conventional economic dispatch models cannot find the most economical generation schedule in power system with different generation zones. Moreover, the most economical schedule of generation cannot satisfy the environmental expectations. Therefore, compromising between generation cost and environmental issues is unavoidable. In this connection, total emission produces by generation units is taken into consideration as an objective function in concert with generation cost function. Furthermore, different operational constraints including valve-point effect, prohibited operating zones and multi-fuel operation are considered to make the proposed approach more realistic. Considering all these restrictions necessitate solving the proposed problem, i.e. multi-objective multi-area economic dispatch problem, by a reliable and strong optimization algorithm. In this regard, a hybrid evolutionary algorithm based on the shuffle frog leaping algorithm and the particle swarm optimization is proposed to solve the proposed problem. Effectiveness of the proposed hybrid algorithm is verified on different test systems.

Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems

Abstract The dearth of power generation from energy resources, environmental concerns and ever-increasing demand for electrical energy necessitate optimal economic dispatch with minimum costs and emissions. Due to the confined optimum convergence and non-convexity of realistic scenarios, classical optimization methods are not proficient to handle such problems. Instead, evolutionary optimization methods have gained more attention in recent years. Application of a new proposed double weighted particle swarm optimization (DWPSO) technique in solving non-convex combined emission economic dispatch (CEED) problems with wind power penetration and also solving non-convex multiple fuel option economic dispatch problem has been technologically proposed in this paper. The results on several case study systems are compared with other published methods in literature and confirm the effectiveness of DWPSO against other existing methods. DWPSO successfully reduces the production costs as well as hazardous emissions considering wind power penetration, selects the best fuel types of the generators and adjusts the feasible and optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using the proposed method can minimize the total generation costs and optimally satisfy the power demands in the grid while the computation performance remains satisfactory even in case of changes in the scale of the network.

An effective Lightning Flash Algorithm solution to large scale non-convex economic dispatch with valve-point and multiple fuel options on generation units

Generation units with multiple fuel options and valve-point loading effects on the generators are fundamental parts of power system generation. However, economic dispatch (ED) of these units have non-convex, non-continuous generation cost functions. In this paper, a new Lightning Flash Algorithm (LFA) is proposed to solve complex non-convex ED problems in large scale power systems considering multiple fuel options and valve point effects on the generators. Five case study systems including 10-units, 40-units, 80-units, 160-units and 640-units are conducted to validate the applicability and effectiveness of the proposed LFA method for solving ED problems. The results are compared with other published methods in literature and confirm the applicability and effectiveness of LFA against other existing methods. LFA can successfully conduct the best fuel types of the generators and adjust the optimum settings to allocate load demand to the online generation units in power system. The results demonstrate that using proposed LFA method can minimize the total generation costs and optimally satisfy the load demands in the power grid.