Lokesh Kumar Panwar

Binary Fireworks Algorithm Based Thermal Unit Commitment

This paper presents the first application of fireworks algorithm to solve thermal unit commitment and scheduling problem. The scheduling problem accompanied by many constraints i.e., equality constraints like load balance and inequality constraints like system reserve and bounds like power generation, up/down time and ramp rate limits, finally shapes into a complex optimization problem. In this work, the scheduling and commitment problem is solved using binary fireworks algorithm BFWA, which mimics explosion of fireworks in the sky to define search space and distance between associated sparks to evaluate global minimum. Further, the effectiveness of fireworks pertaining to problem dimension, wide range of generation units from 10 to 100 are considered and evaluated. In addition, simulations results are compared to the existing optimization techniques in literature used for unit commitment and scheduling problem and it is observed that, BFWA is superior to some of the profound existing algorithms in achieving near optimal scheduling.

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.

Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem

Abstract This paper proposes a quantum inspired binary grey wolf optimizer (QI-BGWO) to solve unit commitment (UC) problem. The QI-BGWO integrates quantum computing concepts with BGWO to improve the hunting process of the wolf pack. The inherent properties of Q-bit and Q-gate concepts in quantum computing help in achieving better balance between exploration and exploitation properties of the search process. The position update processes of wolves at different hierarchy levels are replaced by Q-bits individual probabilistic representation along with dynamic rotation angle and coordinate rotation gate. Therefore, solution approaches exploit the search properties of GWO and quantum computing using quantum bits, gates, superposition principle etc., to solve the unit commitment schedule efficiently. The results and statistical analysis demonstrates the effectiveness of proposed approaches in solving the UC problem and establishes the significance of proposed approaches among existing binary and quantum computing based heuristic 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.

Profit-based conventional resource scheduling with renewable energy penetration

Technological breakthroughs in renewable energy technologies (RETs) enabled them to attain grid parity thereby making them potential contenders for existing conventional resources. To examine the market participation of RETs, this paper formulates a scheduling problem accommodating energy market participation of wind- and solar-independent power producers (IPPs) treating both conventional and RETs as identical entities. Furthermore, constraints pertaining to penetration and curtailments of RETs are restructured. Additionally, an appropriate objective function for profit incurred by conventional resource IPPs through reserve market participation as a function of renewable energy curtailment is also proposed. The proposed concept is simulated with a test system comprising 10 conventional generation units in conjunction with solar photovoltaic (SPV) and wind energy generators (WEG). The simulation results indicate that renewable energy integration and its curtailment limits influence the market participation or scheduling strategies of conventional resources in both energy and reserve markets. Furthermore, load and reliability parameters are also affected.

Potential benefits of electric vehicle deployment as responsive reserve in unit commitment

Modernization of electric grid and deregulation of electricity market put forward many tough challenges both technically and economically. Improving the power system reliability is a major task amongst them, which depends on the responsive reserve capacity and its response time in case of contingency. Fast response time and the possibility of Electric Vehicle (EV) participation in deregulated electricity market environment can be exploited in the deterministic scheduling of EV as responsive reserve (RR) in unit commitment (UC) problem to enhance power system reliability at reasonable cost and facilitating economic feasibility of EV deployment. To investigate this concept conventional thermal units are scheduled considering EV as responsive reserve which aims at minimizing the cost for independent system operator (ISO) in satisfying load and reserve requirements under stringent constraints imposed by generation units and yielding extra income through selling EV capacity into reserve markets to maximize the revenue with lower risk of degradation and charge outage. From the results it is observed that in the present scenario, selling EV into reserve market would fetch more revenue than the energy market counterpart and at the same time EV reserve procurement also minimizes the operational cost for ISO when compared to conventional reserve procurement.

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.

Optimal Offering of Demand Response Aggregation Company in Price-Based Energy and Reserve Market Participation

This paper investigates the combined price-based scheduling/participation of generation company (GENCO) and demand response aggregation company (DRACO) in energy and reserve markets. The temporally coupled customer behavior can be better represented using the load profile attributes, when compared to the traditional approach with random willingness assignment. The proposed cost models for energy and reserve offerings consider the effect of load type, load pattern consumption, and availability/flexibility patterns of each type of load with time of use constraints. The load curtailment (LC) cost model accounts for criticality and willingness of the responsive loads via utilization factor and availability factors, respectively. The proposed cost models present a realistic picture of LC cost by eliminating the random willingness factor of the existing LC cost models. Thereafter, various cases of market participation with different reserve payment policies are formulated for combined participation of GENCO and DRACO. In addition, the sensitivity of participation decision of various entities to the seasonal load variation is examined for summer and winter loading profiles. The proposed cost models and scheduling framework is simulated using GENCO with ten thermal units and DRACO with various load types, profiles distributed across different load sectors comprising of commercial, residential, industrial, municipal, and agricultural loads. The combined participation resulted in improved market surplus with reduced GENCO surplus. Also, the energy and reserve market surplus dependence on seasonal load patterns is observed across different test cases and payment policies.

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.

A multiple emission constrained approach for self-scheduling of GENCO under renewable energy penetration

The electric sector contributes substantially to both greenhouse gas (GHG) and non-greenhouse gas (NGHG) emissions, which means that both conventional and thermal generation companies (GENCOs) must follow certain environmental guidelines to address various emission requirements. This paper presents a methodology to investigate the feasibility of both GHG and NGHG emission reduction in a deregulated electricity market. The proposed model takes into consideration the effect of NGHG emission cost constraints in conjunction with classical GHG emission constraints for the scheduling aspects of GENCO. A profit based self-scheduling problem with conventional fossil fueled generators and renewable energy technologies (RETs) is formulated including emission penalties and avoidance costs of GHG and NGHG emissions, respectively. Thereafter, a set of pareto solutions is evaluated for different possible scheduling scenarios. A simple, effective optimality criteria is also postulated to identify the tradeoff solution. Finally, a sensitivity analysis of various technical, environmental, as well as economic aspects is presented to examine the effect of NGHG consideration and RET inclusion in scheduling. The simulation results are presented and discussed in detail to examine the effect of NGHG consideration in self-scheduling practices of GENCO in the electricity market, thus reflecting the benefits of the proposed approach over classical emission handling approaches.