Tahir Nadeem Malik

An improved chaotic hybrid differential evolution for the short-term hydrothermal scheduling problem considering practical constraints

Short-term hydrothermal scheduling (STHTS) is a non-linear and complex optimization problem with a set of operational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques; however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution (ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution (DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as compared with other recently established evolutionary approaches.

Probablistic generation model for optimal allocation of PV DG in distribution system with time-varying load models

Distributed generation (DG) planning studies are generally conducted while assuming constant generation and load models. However, in this paper, optimal allocation of photovoltaic (PV) based DG is carried out while considering probabilistic generation and time varying voltage dependent load models. First, a novel probabilistic generation model is proposed for solar irradiance uncertainty modeling to compute the hourly output power produced via PV units. Subsequently, the obtained power values are used to determine the optimum PV allocation in the distribution system through particle swarm optimization to minimize the multi-objective function. The proposed algorithm has been validated on 33-bus and 69-bus distribution test systems. The results demonstrate that the proposed model is suitable for solar irradiance modeling and can be employed in power system planning studies.

Evaporation rate based water cycle algorithm for the environmental economic scheduling of hydrothermal energy systems

The environmental concerns are gaining importance nowadays for power producing companies in the context of clean energy act. The efforts of reducing the harmful emissions beyond a certain level are being made by most of the power utilities in the developing countries. Consequently, the minimization of harmful emissions has also become an important objective function while solving the hydrothermal scheduling problem. The heuristic approaches are considered as potential solution methodologies for non-convex hydrothermal scheduling. This paper presents an Evaporation Rate based Water Cycle Algorithm (ERWCA) for the solution of non-convex Environmental Economic Scheduling of Hydrothermal Energy Systems (EESHES). This algorithm has been evolved from the water cycle nature of raining, formation of streams, and their movement towards the rivers and finally into the sea. ERWCA has been investigated on the standard test system of EESHES with three case studies: (i) Economic Cost Scheduling, (ii) Environmental Economic Scheduling, (iii) Economic Environmental and Cost Scheduling. The comparison of obtained results with the recent results in the literature shows the superiority of ERWCA in terms of both lower fuel cost and fuel emissions. Hence, ERWCA is a worthwhile addition to the algorithms which have successfully solved this complex and combinatorial bi-objective optimization problem.

Multirate Signal Processing: Some Useful Graphical Results

Multirate signal processing is a phenomenon in which the sampling rate of the signal is changed in order to increase the efficiency of the various signal processing operations. Undersampling, reduces the sampling rate, whereas oversampling, followed by interpolation, increases the sampling rate. The ability to process signals at multiple sampling rates has made it possible to reduce the costs and improve performance. In this paper a basic overview of sampling is presented. Then different possibilities have been explored in terms of undersampling and oversampling. Graphical interpretations have led to some interesting results.

Economic Dispatch Using Genetic Algorithm Based Hybrid Approach

Power Economic Dispatch (ED) is vital and essential daily optimization procedure in the system operation. Present day large power generating units with multi-valves steam turbines exhibit a large variation in the input-output characteristic functions, thus non-convexity appears in the characteristic curves. Various mathematical and optimization techniques have been developed, applied to solve economic dispatch (ED) problem. Most of these are calculus-based optimization algorithms that are based on successive linearization and use the first and second order differentiations of objective function and its constraint equations as the search direction. They usually require heat input, power output characteristics of generators to be of monotonically increasing nature or of piecewise linearity. These simplifying assumptions result in an inaccurate dispatch. Genetic algorithms have used to solve the economic dispatch problem independently and in conjunction with other AI tools and mathematical programming approaches. Genetic algorithms have inherent ability to reach the global minimum region of search space in a short time, but then take longer time to converge the solution. GA based hybrid approaches get around this problem and produce encouraging results. This paper presents brief survey on hybrid approaches for economic dispatch, an architecture of extensible computational framework as common environment for conventional, genetic algorithm & hybrid approaches based solution for power economic dispatch, the implementation of three algorithms in the developed framework. The framework tested on standard test systems for its performance evaluation.Copyright

Biogas supported bi-level macro energy hub management system for residential customers

In energy distribution systems, micro hubs are concerned about cost, whereas utilities or macro energy hubs (MEHs) are sensitive to network load deviations. This paper investigates the nature of the customer-utility relationship using a two-step methodology. In the first step, a two-tier centralized MEH model is proposed for residential customers. In the second step, a mathematical model describing the nature of the cost-deviation relation is developed using the bi-variate regression analysis technique. The MEH model is solved using particle swarm optimization (PSO) and flower pollination algorithm (FPA) in MATLAB and proved that FPA is superior to both PSO and mixed integer linear programming. The analysis of the results shows that the cost-deviation relationship is complex, non-linear, and random in nature. Due to cost-deviation non-linearity, the deviation inflates while minimizing cost at the building level. Therefore, deviation minimization is necessary to ensure network stability. At the network lev...

Impact of time varying load models on PV DG planning

A constant load and generation model is generally considered in planning studies that pertain to Distributed Generation (DG) integrated distribution systems. However, such considerations may result in misleading and inconsistent values for the voltage profile, loss reduction, payback period, deferral values, and other related calculations due to the variation of both renewable energy-based generation and load demand. This paper investigates the impact of time varying voltage dependent load models on the DG planning studies. The study is based on the comparative assessment of different impact indices, penetration level, active and reactive power intake, active and reactive power loss, and Mega Volt Ampere support offered by the installation of photovoltaic based DG for different time varying load models. The outcomes of the current research work reveal that the time varying load modeling approach has significant impact on the distribution systems.A constant load and generation model is generally considered in planning studies that pertain to Distributed Generation (DG) integrated distribution systems. However, such considerations may result in misleading and inconsistent values for the voltage profile, loss reduction, payback period, deferral values, and other related calculations due to the variation of both renewable energy-based generation and load demand. This paper investigates the impact of time varying voltage dependent load models on the DG planning studies. The study is based on the comparative assessment of different impact indices, penetration level, active and reactive power intake, active and reactive power loss, and Mega Volt Ampere support offered by the installation of photovoltaic based DG for different time varying load models. The outcomes of the current research work reveal that the time varying load modeling approach has significant impact on the distribution systems.

Dynamic Economic Dispatch Incorporating Photovoltaic and Wind Generation using Hybrid FPA with SQP

ABSTRACT This paper presents a hybrid approach to explore the effect of wind and photovoltaic energy on conventional power network using dynamic economic dispatch. This model deals with stochastic and unpredictable character of wind and solar power output while considering the gaps between the real and predicted output power. A hybrid flower pollination algorithm with sequential quadratic programming is applied to reduce the overall operating costs of generators, while allocating generation to the committed units. The proposed technique is applied on power system having 13 solar units and a wind plant connected with 3, 5, and 10 generating units, respectively. The results of algorithm simulations performed in MATLAB 2014b confirm the competence of the suggested method.

Economic analysis of net metering regulations for residential consumers in Pakistan

Net metering is used to incentivize the distributed generation owners. It is introduced in Pakistan with the aim to promote the building integrated local generation. Presently, it is hard to find any study on the economic incentive indicators of the net metering policy for residential customers in Pakistan. This paper presents the economic evaluation of net metering benefits to the individual residential consumers in the presence of Building Integrated PV (BIPV) system under current net metering regulations in Pakistan. The energy demand of the individual apartments and the common area services inside the building is calculated by means of daily energy usage of the residents for a typical day. The aggregate demand of the residential building, comprising of 100 residential units and common area services, is calculated. The estimation of power profiles of the PV generation system is carried out with the help of PVGIS. At the end, the economic analysis of the proposed net metering scheme is presented. The net metering policy is found to be feasible up-to 50 kWp PV capacity when it is applied on the common area services only and the billing is carried out individually. When it is implemented on the aggregate energy demand of the entire residential building, the annual savings are observed for installed PV capacity above 80 kWp.

Short Term Economic Emission Power Scheduling of Hydrothermal Energy Systems Using Improved Water Cycle Algorithm

Due to the increasing environmental concerns, the demand of clean and green energy and concern of atmospheric pollution is increasing. Hence, the power utilities are forced to limit their emissions within the prescribed limits. Therefore, the minimization of fuel cost as well as exhaust gas emissions is becoming an important and challenging task in the short-term scheduling of hydro-thermal energy systems. This paper proposes a novel algorithm known as WCA-ER (Water Cycle Algorithm with Evaporation Rate) to inspect the short term EEPSHES (Economic Emission Power Scheduling of Hydrothermal Energy Systems). WCA has its ancestries from the natural hydrologic cycle i.e. the raining process forms streams and these streams start flowing towards the rivers which finally flow towards the sea. The worth of WCA-ER has been tested on the standard economic emission power scheduling of hydrothermal energy test system consisting of four hydropower and three thermal plants. The problem has been investigated for the three case studies (i) ECS (Economic Cost Scheduling), (ii) ES (Economic Emission Scheduling) and (iii) ECES (Economic Cost & Emission Scheduling). The results obtained show that WCA-ER is superior to many other methods in the literature in bringing lower fuel cost and emissions.