Exploiting advanced genetic algorithm technique in optimal scheduling of pumped storage hydropower plant and wind farms in unit commitment program

An efficient, robust optimization model for the unit commitment considering renewable uncertainty and pumped-storage hydropower

Power system operator executes the unit commitment (UC) program to determine the optimal generation schedule for a daily or weekly horizon. In smart power systems, cyber-attacks such as false data injections (FDIs) may resemble a kind of contingency with only manipulating measurements data and without physical initiating events. This paper proposes a network constrained UC model in the presence of cyber-attacks named cyber-secured UC (CSUC) model. Normally the operating point obtained by UC program is on its conventional schedule, however in case of cyber-attack risk, it can depart from the conventional schedule toward the cyber secured schedule without violating unit and network constraints during such transition. The CSUC schedule is secured against a type of cyber-attacks called load redistribution (LR) attacks. Moving to this new operating point, in case of cyber-attack risk, may incur a small and reasonable additional cost to the cost of conventional generation schedule. In order to guarantee the optimality of solution, the proposed CSUC model is formulated as a mixed integer linear programming (MILP) model. The benders decomposition (BD) method is utilized to solve the model in a master and sub-problem hierarchy. The proposed method is implemented in the IEEE 118-bus test system.

Frequency control of hybrid wind-hydro power plants with long conduits in isolated power systems

In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulation (MCS) is employed to select several scenarios with specific probability coefficients. Additionally, two innovative objective functions based on operation cost and emissions are introduced, with each scenario weighted based on its occurrence probability. To assess the performance of the proposed MOGOA-MBES algorithm, simulations are conducted across three scenarios with varying conditions, and the results are compared against those obtained from several multiobjective algorithms. Our findings, supported by optimization results and the S-metric index, demonstrate that the proposed MOGOA-MBES algorithm outperforms other algorithms in terms of reducing operation cost and emissions. Furthermore, the simulation results reveal that uncertainties lead to an increase in cost and emissions, whereas the inclusion of flexible loads and their participation in the UC program can effectively mitigate cost and emission levels.

The significant ongoing technical and scientific advancements in renewable generation technologies and developments in smart grids' infrastructures have reduced their prices and costs to an affordable economical level. Hence, in the last decade, integration of renewable energies has had conspicuous pervasiveness, and the structures of power networks have been evolving to smart grid styles. The intelligently operated grids and more penetration of green energies facilitate alleviation of the generation expenditures, mitigation of greenhouse gases emissions, and achieving more efficient exploitation of installed generation resources. Aside from various uncertainties in power system operation, the inclusion of renewable resources, which are mainly intermittent in nature, encounters the power system operation scheduling with severe challenges. Therefore, the uncertainties of renewable resources such as wind and solar energy resources, as well as inherent uncertainties of power systems such as load forecast inaccuracies must be included mathematically in the operation schedules. The consideration of such uncertainties improves the robustness against plausible volatilities and contingencies and provides a more secure operation. The incorporation of various uncertainties into the unit commitment program deteriorates the solution of the problem in term of complexity. The solution of such a sophisticated problem which comprises time-oriented and practical constraints requires either appropriate exact or heuristic approaches. In this paper, a 10-generator test system is selected for simulations, and the virus colony search (VCS) algorithm is employed to solve unit commitment problem considering the impact of the presence of intermittent wind farms. Ultimately, the economic dispatch is performed between committed units, and operational costs are also calculated. The uncertainties of wind and load forecasts are incorporated in the system modeling through three scenarios. These scenarios demonstrate the impact of the presence of wind units in smart grids, and the way uncertainties affect the electricity network operation economically. Besides, the way wind farms must treat with such uncertainties through appropriate bidding strategy is investigated in order to protect themselves from high prices of spot market and plausible detriments.

Reliability and economic evaluation of demand side management programming in wind integrated power systems

Wind Energy has become an astonishing power in the world. Nowadays people are realizing the importance of renewable energies, especially wind energy. Though we are extracting power from the wind, it is not extracted completely because of the load variation. Many wind turbines are made to turn off in case of high wind or low load. The solution for this problem is to coordinate wind plant with the hydro power plant especially pumped storage power plant. India has a good potential of wind energy remarkably in Tamilnadu. The wind farm is coordinated with the Pumped storage hydro power plant at Tehri in Uttarkhand, India. It is done with the help of PMU. The coordination is done with the help of MATLAB/SIMULINK.

A thermal unit commitment (UC) computer program has been used at the Riyadh Energy Control Centre of Saudi Consolidated Electric Company, Central Region, (SCECO Central) since 1982. Since then, system growth, plant aging, turbine degradation, ambient temperature effect on unit output, transmission constraints (line loading), MVAr/voltage control problem at light loads, and stringent operating requirements (spinning reserve, etc.) have imposed many constraints on the program, making it nearly impossible to produce a generation allocation schedule that is acceptable to system operators. A study undertaken to decide what should be done with the UC program and how the operator could be supplied with a daily generation schedule that is sufficiently accurate and practically feasible is described. Because it was not feasible to provide a new unit commitment program or to modify the existing one, it was decided to tailor the unit commitment input dataset such that the constraints are appropriately represented. In addition, it was decided to write a suite of computer programs (UCPROG) in FORTRAN that would process necessary input data and results of the UC program to achieve a more realistic, detailed, but precise unit allocation schedule.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This study presents the application of an improved self-organizing migration algorithm (ISOMA) for minimizing the total electricity production expenditure (TEPE) and maximizing the total electricity sale profit (TPRF) for hydrothermal power systems (HTPS) without and with renewable energies. Two power system configurations were employed to test the real efficiency of ISOMA while dealing with two objective functions. In the first configuration, there was one thermal power plant and one hydropower plant, while in the second configuration, wind and solar energy were both connected to the first system. The results achieved in the first configuration with the first objective function indicated that ISOMA not only outperformed SOMA according to all comparison criteria but was also superior to other methods such as evolutionary programming (EP), acceleration factor-based particle swarm optimization (AFPSO), and accelerated particle swarm optimization (APSO). The evaluation of the results achieved by ISOMA in the second configuration with the objective function of maximizing the TPRF revealed that ISOMA could reach better profits than SOMA in terms of maximum, mean and minimum TPRF values over fifty trial runs. As a result, it was concluded that pumped storage hydropower plants are very useful in integrating with renewable power plants to cut total cost for thermal power plants and in reaching the highest profit for the whole system. Also, ISOMA is a suitable algorithm for the considered problem.

Lightning as a natural phenomenon represents directly and indirectly a risk for wind turbines and other objects as the transient voltages may reach dangerous levels for people, equipment, and transmission lines. It is important to consider lightning and its consequences on the placement of wind turbines, buildings, and other sensitive electrical installations. The objective of this study is to optimize the placement of a wind farm for minimum lightning occurrence on the turbines. The method is also applied to optimize the location of any other sensitive installation for minimum electromagnetic interference from lightning current in any geographic area. The process involves gathering accurate lightning data information for the interested area from North American Lightning Detection Network (NALDN) over a period of time and using the genetic algorithm optimization technique using MATLAB to determine the wind farm location and other sensitive equipment placement. It is found that genetic algorithm optimization technique produced results which are very consistent considering different scenarios. The findings in this study could help wind farm designers in addition to other criteria for energy collecting efficiency of wind turbines in order to determine the best location of wind farms.

Review of HVAC scheduling techniques for buildings towards energy-efficient and cost-effective operations

An expert system has been developed by combining the knowledge of experienced power system operators and unit commitment experts to assist operators in scheduling thermal generating units. The expert system is used as a preprocessor as well as a postprocessor to the truncated dynamic-programming based unit commitment program to obtain an operationally feasible and/or preferable solution. The operator's interaction with the expert system is minimised by transferring all the relevant data and results of the unit commitment program to the expert system as a knowledge base. Some of the complex operating constraints that are not violated frequently and/or are difficult to include in the unit commitment program are enforced by the expert system.

This paper presents an approach to the problem of dispatch and unit commitment of wholly owned and commonly owned units. Operatinag strategies are described and modifications to conventional dispatch and unit commitment programs are examined. Dynamic programming is used as the basic technique to determine an optimum schedule for startup and shutdown of available generating units in addition to unit base generation and production costs.

This paper takes Unit Commitment with Wind Power Electric Systems for instance and studies the application of relevant indicators in the short run adequacy decisions. It first conducted running adequacy assessment of the RBTS system, and then given unit combination solutions in the ample index constraints. The results show that the relevant indicators can be more accurately to depict the influence of random factors on system operation adequacy, and Unit Commitment decisions based adequacy index is able to give a unit commitment program at different adequacy levels. The research results provide a set of assessment indicators and assessment methods for short running adequacy problems of random fluctuations power accessing to large-scale power systems..

Variable-speed (VS) pumped storage technology has become a new trend for providing better support power system incorporating renewable energies. On the other hand, the transient stability of the power system can be affected by the application of such kind of storage unit. State-of-the-art pumped storage hydropower plants (PSHP) based on doubly fed induction machine (DFIM) known as variable-speed and conventional PSHP-based on synchronous machine (SM) known as fixed speed (FS) have a different effect on transient stability of a large-scale power system. This chapter intends to present the modelling and controller of DFIM-based PSHP of two types of PSHPs, i.e., VS and FS under generating operation mode in DIgSILENT software. Also, the IEEE 10-machine 39-bus system namely New England test system is adopted as a large power network. The results show that using DFIM-based VS-PSHP in the interconnected power grids, not only the oscillation modes of PSHP is eliminated, but also it can strongly improve rotor angle and voltage transient stability of the power system.