The thermal unit commitment (TUC) in regulated and competitive electric power markets aims to minimise the system-wide operational costs of generators by providing an optimal power generation schedule so that the forecasted power demand should be equalled. This process is formulated mathematically as a non-linear, large-scale, mixed-integer combinatorial optimisation problem, which is quite difficult due to its inherent high-dimensional, non-convex, discrete and nonlinear nature. Now-a-days, the inclusion of pollutant emission, ramp rate limits and forced outage rate in the TUC problem is being an appreciative insight in the fossil-fuel scarcity scenario. To address all these aspects, the progressive TUC (PTUC) process is developed which increases further the complexity in finding the best feasible solution. Recently, in the field of evolutionary computations, an innovative optimisation algorithm, namely grey wolf optimisation (GWO), has been developed by inspiring the behaviour of grey wolves. GWO does not have any affinity to stick in local optimum points in the complex multimodal optimisation problem, and it provides a more diverse search of the solution space. In order to handle the operational constraints of PTUC problem effectively, the real-coded scheme is introduced in GWO, and in this context, real coded GWO (RCGWO) is implemented to solve the various PTUC problems under single and multi-objective frameworks. The real-coded scheme is developed using the load curve, and it seems to be particularly natural when tackling optimisation problems of parameters with variables in continuous domains. The standard and practical systems are used for demonstration, and the performance analysis of RCGWO confirms that the RCGWO is robust and consistent in finding the ever reported best feasible TUC solution. The RCGWO provides more reliable unit commitment (UC) decisions on fuel consumption, emission allowance, reliable operation of power system and long-term utilisation of generating units. This could devise a set of corrective/preventive control actions for the secure, reliable, social welfare and economical operation of power generation systems. Hence, the RCGWO-based UC solutions enable the utility to obtain an extra value and cope easier with the demands of energy economics.

Chapter Contents:

• Abstract
• 12.1 Soft computing techniques
• 12.1.1 Evolution of algorithms
• 12.1.2 Modern bio-inspired algorithms
• 12.2 Grey wolf optimisation algorithm- brief
• 12.2.1 Development of GWO
• 12.2.2 Mathematical modelling of GWO
• 12.2.2.1 Social hierarchy
• 12.2.2.2 Encircling prey
• 12.2.2.3 Hunting
• 12.2.2.4 Attacking the prey
• 12.2.2.5 Exploration
• 12.3 Real-coded GWO to thermal unit commitment
• 12.3.1 Thermal unit commitment model
• 12.3.1.1 Minimisation of total operating cost
• 12.3.1.2 Minimisation of pollutant emission
• 12.3.1.3 Progressive thermal UC model
• 12.3.1.4 System and operational constraints
• 12.3.2 Prior art for cost-effective thermal UC
• 12.3.3 Development of real-coded GWO
• 12.3.4 Managing multiple objectives and constraints
• 12.3.5 RCGWO implementation for UC solution
• 12.4 Validation of RCGWO through practical/standard test systems
• 12.4.1 Cost-effective operation
• 12.4.2 Economic/environmental operation
• 12.4.3 Economic/environmental/reliable operation
• 12.4.4 Practical case studies
• 12.4.4.1 Tai power system
• 12.4.4.2 Uttar Pradesh State Electricity Board System
• 12.5 Performance evaluation of RCGWO
• 12.5.1 Selection of search agents
• 12.5.2 Robustness test
• 12.5.3 Statistical measures
• 12.6 Summary
• References

Inspec keywords: fuel economy; power generation economics; integer programming; energy consumption; power markets; power generation scheduling; power generation reliability; power generation dispatch; codes; power system security; thermal power stations; combinatorial mathematics; concave programming

Other keywords: nonlinear large-scale mixed-integer combinatorial optimisation problem; power generation systems; fossil-fuel scarcity scenario; PTUC problem; fuel consumption; real-coded grey wolf optimisation algorithm; pollutant emission; optimal power generation scheduling; progressive thermal power system unit commitment; mathematical analysis; competitive electric power market regulation; energy economics; evolutionary computation; power demand forecasting; complex multimodal optimisation problem; RCGWO-based UC solutions

Subjects: Combinatorial mathematics; Thermal power stations and plants; Power system management, operation and economics; Optimisation techniques; Reliability; Power system control