Srikanth Reddy K

Binary Grey Wolf Optimizer for large scale unit commitment problem

Abstract The unit commitment problem belongs to the class of complex large scale, hard bound and constrained optimization problem involving operational planning of power system generation assets. This paper presents a heuristic binary approach to solve unit commitment problem (UC). The proposed approach applies Binary Grey Wolf Optimizer (BGWO) to determine the commitment schedule of UC problem. The grey wolf optimizer belongs to the class of bio-inspired heuristic optimization approaches and mimics the hierarchical and hunting principles of grey wolves. The binarization of GWO is owing to the UC problem characteristic binary/discrete search space. The binary string representation of BGWO is analogous to the commitment and de-committed status of thermal units constrained by minimum up/down times. Two models of Binary Grey Wolf Optimizer are presented to solve UC problem. The first approach includes upfront binarization of wolf update process towards the global best solution (s) followed by crossover operation. While, the second approach estimates continuous valued update of wolves towards global best solution(s) followed by sigmoid transformation. The Lambda-Iteration method to solve the convex economic load dispatch (ELD) problem. The constraint handling is carried out using the heuristic adjustment procedure. The BGWO models are experimented extensively using various well known illustrations from literature. In addition, the numerical experiments are also carried out for different circumstances of power system operation. The solution quality of BGWO are compared to existing classical as well as heuristic approaches to solve UC problem. The simulation results demonstrate the superior performance of BGWO in solving UC problem for small, medium and large scale systems successfully compared to other well established heuristic and binary approaches.

Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique

Abstract This paper presents an intelligent computational technique, modified moth-flame optimization algorithm (MMFOA) to examine the exploration and exploitation characteristics of basic MFOA approach. Additionally, the binary coded variants of basic as well as MMFOA namely binary coded modified moth flame optimization algorithms (BMMFOA) are developed for solving unit commitment (UC) problem. The moth-flame algorithm is a nature inspired heuristic search approach that mimics the traverse navigational properties of moths around artificial lights tricked for natural moon light. Unlike many other swarm based approaches, the position update in MFOA is a one-to-one procedure between a moth and corresponding flame. In the basic MFOA, to improve exploitation characteristics of moths, the flame number is reduced as a function of iteration count. The last flame with worst fitness is then duplicated to serve as position update reference for left over (excess) moths. The four additional variants proposed in this paper includes different flame selection procedures based on balance between exploitation and exploration aspects of search process. The proposed BMMFOA variants are tested on unit commitment problem of power system operational scheduling. The binary mapping of continuous/real valued moth, flame locations for solving UC problem is carried out using modified sigmoidal transformation. The efficacy of the proposed BMMFOA against basic MFOA and other approaches for various test systems is analysed in terms of solution quality, execution time and convergence characteristics. Also, several standard statistical tests such as Friedman (aligned and non-aligned), Wilcoxon and Quade test are used to establish statistical significance of BMMFOA among existing approaches and basic MFOA.

Modeling of Carbon Capture Technology Attributes for Unit Commitment in Emission-Constrained Environment

This paper discusses the modeling and analysis of a carbon capture technology-based resource scheduling/unit commitment (UC) methodology in carbon/emission-constrained environment. In carbon markets, the overall generation from conventional/fossil-fueled thermal plants is constrained over the total cost which is the sum of generation, capture cost/emission avoidance cost. Therefore, in this paper, generalized/uniform performance indices affecting the UC schedule are derived. Also, a commitment/scheduling methodology based on capture and fuel cost is devised rather than the existing penalty cost methodology as in the modernized markets everything was dealt in monetary value. Furthermore, the impact of type of resource used, i.e., coal rank is also considered to evaluate the sensitivity of scheduling decisions and financial influence of carbon capture technology. Along with performance indices, correction factors are also proposed to justify the effect of resource/coal rank in the process of optimal generation allocation. The effect of correction factors and type of coal is observed to be predominant at lower capture efficiencies compared to higher capture efficiency. The effectiveness of the proposed method over the penalty method is reflected in reduced generation cost and emission avoidance cost when compared to the penalty-based methodology.

Computational Intelligence for Demand Response Exchange Considering Temporal Characteristics of Load Profile via Adaptive Fuzzy Inference System

This paper presents a computationally intelligent hybrid approach to incorporate the temporal characteristics of customer baseline load (CBL) in demand response exchange (DRX) mechanism using adaptive fuzzy inference system (FIS). The proposed hybrid approach considers the temporal characteristics of load profile using utilization factor, availability factor alongside the conventional/traditional willingness factor. The relation between load criticality and flexibility in terms of utilization and availability factors has been established and incorporated into the DR seller/customer bids in DRX through dynamic costing. Various models viz. linear, nonlinear, and exponential model etc., are developed to assess varying behavior of customer with respect to the CBL profile. In addition, a FIS is developed in this paper to account for uncertain/indistinct nature of input/information provided by the customer. To improve the performance of DRX market clearing, parameters of membership functions used in FIS are adaptively tuned using heuristic approaches. The performance of proposed hybrid model using FIS is compared with the traditional approach, fuzzy, and nonfuzzy approach without considering temporal characteristics, fuzzy, and nonfuzzy approaches with temporal characteristics only. The simulation results are presented and they demonstrate the superiority of FIS based hybrid model with CBL temporal characteristics through dynamic costing when compared to other models.

Optimal scheduling of uncertain wind energy and demand response in unit commitment using binary grey wolf optimizer (BGWO)

The uncertain wind energy handling is a vital aspect of modern power system operational planning with considerable wind penetration. The handling of same is a techno-economic constrained procedure considering its effect on energy and reserve scheduling of whole generation mix. This paper presents a dynamic penalty cost based methodology to solve power system resource scheduling problem with uncertain wind energy, thermal units and responsive loads using binary grey wolf optimizer (BGWO). The proposed methodology considers the effect of uncertain wind energy in terms of total rescheduling cost, total energy balancing cost and total reserve cost. The unit commitment procedure is solved using BGWO and the economic dispatch of committed thermal units alongside the wind energy, responsive load scheduling is solved using Lambda iteration technique. In addition, two different levels of wind uncertainty level are considered to examine the variation techno-economic aspects of proposed methodology. The simulation results are presented and discussed with respect to various performance attributes and the same demonstrate the superior performance of proposed dynamic penalty cost models over existing static cost models.

Binary grey wolf optimizer models for profit based unit commitment of price-taking GENCO in electricity market

Abstract The restructuration of electric power sector has renovated the power system operational planning. In the deregulated market, electricity is treated as an entity unlike in the traditional vertically integrated market model where it is treated as a service provided by the generation companies (GENCOs). The task of GENCO is to perform self-scheduling of available units so as to achieve maximum profit in restructured power sector. Therefore, the problem of profit based unit commitment (PBUC) in deregulated markets can be formulated as a commitment and generation allocation through self-scheduling procedure. The commitment schedule optimization, i.e whether the status of a thermal unit is to be changed to on or off state, is a binary problem. Thus, the PBUC problem requires binary optimization for commitment and real valued optimization for generation allocation. In this paper, three binary grey wolf optimizer (BGWO) models are presented to solve the profit based self-scheduling problem of GENCO. The BGWO models proposed in this paper differ with respect to the transformation function used to map real valued wolf position into a binary variable. The binary mapping of commitment status is carried out using sigmoid and tangent hyperbolic transfer functions. Also, in the sigmoidal transfer function, two binary transformation procedures, namely crossover and conventional sigmoidal transfer function, are presented. The effectiveness of the proposed models in improving the solution quality of PBUC problem is examined using two test systems, a 3 unit and a 10 unit test system. In addition, two cases of GENCO market participation with and without reserve market participation are simulated. In the test case with reserve market participation, two commonly used reserve payment models are examined. Simulation results are presented and compared to existing approaches that have been used to solve the PBUC problem. The simulation results and statistical analysis demonstrate the improved solution quality (profit or fitness value) of the PBUC problem and statistical significance of the BGWO models with respect to solution quality obtained as compared to other established meta-heuristic approaches.

Experimental analysis of parameter variation and power enhancement of concentrated PV module

Ever increasing energy requirements and rising emission levels have been driving the electricity sector towards more sustainable and eco-friendly methods of power generation. Efficiency improvement of photovoltaic (PV) cell through every possible method is in focus to improve the energy economics of solar PV technology. Concentrated solar PV systems are one of the contenders for high efficiency solar PV systems. However, along with power enhancement, other parameters like open circuit voltage, short circuit current and fill factor of the solar cells that are used in the module are subjected to variation with concentrated solar PV systems. For an inverter, along with power input, the maximum voltage and current inputs are equally important for effective operation. The variation of these parameters in non-linear fashion makes mathematical modelling difficult and complex. Therefore, in the present experiment, variations in voltage, current, fill factor and module temperatures are examined using V-Trough concentrating system. From the experimental results, the actual voltage dependence on temperature and intensity is found to be more compared to the theoretical estimation. Interesting variation of photovoltaic current of concentrated module has been observed during evening times. Fill factor and thermal parameter variations like front and back surface temperature with concentration are simulated and presented along with practical results.

Optimal demand response allocation in resource scheduling with renewable energy penetration

In the emerging smart grid technologies, resource utilization is diversified and responsive demand became an important tool in availing various benefits. The informed load curtailment is a popular means of demand response aspect with customers being facilitated by financial incentives for offered load reduction. Therefore, augmentation of such responsive loads with unit commitment process can be effective in resource scheduling of smart grid. This paper attempts to develop a binary fireworks algorithm application solve unit commitment problem along with demand response and wind energy. While the wind energy is considered as non schedulable, load curtailment is modelled as schedulable through demand response aggregator. Further, system and thermal unit constraints applicable for wind energy and DR are modified. The proposed methodology is tested on system with coal fired thermal generation, demand response aggregator and wind energy. Various possible scheduling scenarios are laid out for examination. Simulation results are presented and discussed in detail. Results shows the effectiveness of demand response in reducing the operational cost of system operator. Also, the demand response showed betterment when scheduled along with wind energy. The reduction in cost from from traditional thermal unit scheduling is observed to be 6.5% which is best among all the possible scenarios.

Optimal schedule of plug in electric vehicles in smart grid with constrained parking lots

Global warming and climate changes propelled many clean technologies across various fields. The electrification of transportation is considered to be one of the key approaches that can curtail the pollution levels prevailing across global fraternity. Electric vehicle (EV) technologies gained substantial importance in this context. The versatility of EV acting as source and sink in a power system projected many challenges. This paper examines one of such challenge i.e., optimal charge-discharge scheduling of EV along with conventional thermal unit commitment (UC). A modified particle swarm optimization (MPSO) is developed to solve the optimal schedule of EV. In addition, a binary version of MPSO is also developed to determine commitment schedule of thermal units. Results confirm the effectiveness of MPSO in solving UC problem compared to some of the benchmark optimization approaches used to solve UC problem.

V-Trough Concentrator with Back Surface Cooling for Rooftop Photovoltaic System

The ever increasing demand for energy and rapid raise in global pollution levels has driven the energy generating sectors towards the search for more sustainable and eco-friendly methods to generate it. Solar energy is the best potential candidate to drive the global energy demand as well as to curb the pollution levels in earth. The photo voltaic and solar thermal systems are the widely developed present technologies to extract the solar energy for the use of mankind. The photo voltaic systems are widely employed around the world due to its capability of converting the available solar energy into the electrical energy which is the most flexible energy available currently on earth. But the commercial adoption of photo voltaic systems on a wide scale is still dented by the limitation of their lower efficiencies. Hence the methods for improvement in efficiency is on focus to improve the economics of these systems. Apart from the power enhancement (for improving the efficiency), other parameters like open circuit voltage, short circuit current, fill factor are also important to consider in the technical point of view viz., for inverter & inter connection with grid systems, performance assessment. In this present work the variation in the above mentioned parameters with the provision of V-trough concentrator arrangement for a module under consideration along with back surface cooling provision is analyzed through the day. Fill factor and efficiency variations were simulated and presented along with practical results.