Demand response scheduling in SCUC problem for solar integrated thermal system using firefly algorithm

Global electricity market deregulation makes compatible changes and new challenges in power system operation planning problem. Maintenance is required for the generating unit to reduce the risk of capacity outage and to improve availability of units and thereby extending equipment lifetime. Modified particle swarm optimization (MPSO) for the generator maintenance scheduling (MS) generates optimal, feasible solution and overcomes the limitation of the conventional methods such as extensive computational effort which increases exponentially as the size of the problem increases. The objective of this paper is to reduce the loss of load probability (LOLP) and maximize the profit of generating units using levelized risk method (LRM). Market participants submit the MS proposal based on market clearing price (MCP) and they request permission and receive approval for planned maintenance outages from the independent system operator (ISO) in competitive electricity markets. Mainly, we are concerned with a primary framework for ISO's maintenance coordination in order to determine LOLP values in the maintenance time intervals using LRM that uses LOLP convolution algorithm. The ISO will put forward its best endeavor to adjust individual generator maintenance schedules according to the estimated LOLP values. The proposed method is tested on five generating companies model of IEEE reliability test system (RTS) to demonstrate the effectiveness of the proposed method and the applicability of the elucidation scheme for large-scale MS coordination problems.

The unit commitment operation in power systems is a complex, non-linear, constrained optimization problem. The commitment and de-commitment decision presents a binary problem which needs discrete/binary optimization approaches. This paper presents a binary bat search algorithm (BBSA) to solve unit commitment (UC) problem. The bat search algorithm belongs to a meta heuristic class of optimization approaches inspired by natural echolocation behavior of bats. In order to solve binary UC problem, the real valued bat search process is mapped to binary search space using sigmoidal transformation function. The BBSA is then applied to test system with 10 thermal units. The effectiveness of the BBSA is verified against system dimension using test systems upto 100 units. Extensive numerical experiments are performed to test the effectiveness of BBSA and statistical analysis of simulation results are presented. The simulation results are presented, discussed and compared to various existing classical and heuristic approaches. The same demonstrate the superior performance of BBSA approach in solving UC problem.

A deregulated multi-objective generation expansion planning (DMGEP) has been modeled in the previous research. The research used loss of load probability (LOLP) as a reliability objective function. LOLP is a probabilistic reliability index that has a disadvantage on computation time. On the other hand, there is a deterministic reliability index that has an advantage on computation time. The deterministic reliability index is reserve margin (RM). This study will conduct a DMGEP by using two reliability index scenarios. The scenarios are LOLP scenario and RM scenario. This research aims to analyze the impact of using RM on DMGEP. To do the impact analysis, it is needed a comparison between LOLP scenario results and RM scenario results. Based on the results of both scenarios, it can be known the impacts of RM scenario. Using RM can accelerate computation time by 70.7% and cause additional total installed capacity of power plants. The total installed capacity in the RM scenario is higher of 3.2% than the LOLP scenario.

In recent years, high penetration of variable generating sources, such as wind power, has challenged independent system operators (ISO) in keeping a cheap and reliable power system operation. Any deviation between expected and real wind production must be absorbed by the power system resources (reserves), which must be available and ready to be deployed in real time. To guarantee this resource availability, the system resources must be committed in advance, usually the day-ahead, by solving the so-called unit commitment (UC) problem. If the quantity of committed resources is extremely low, there will be devastating and costly consequences in the system, such as significant load shedding. On the other hand, if this quantity is extremely high, the system operation will be excessively expensive, mainly because facilities will not be fully exploited. This thesis proposes computationally efficient models for optimal day-ahead planning in (thermal) power systems to adequately face the stochastic nature of wind production in the real-time system operation. The models can support ISOs to face the new challenges in short-term planning as uncertainty increases dramatically due to the integration of variable generating resources. This thesis then tackles the UC problem in the following aspects: Power system representation: This thesis identifies drawbacks of the traditional energy-block scheduling approach, which make it unable to adequately prepare the power system to face deterministic and perfectly known events. To overcome those drawbacks, we propose the ramp-based scheduling approach that more accurately describes the system operation, thus better exploiting the system flexibility. UC computational performance: Developing more accurate models would be pointless if these models considerably increase the computational burden of the UC problem, which is already a complex integer and non-convex problem. We then devise simultaneously tight and compact formulations under the mixed-integer programming (MIP) approach. This simultaneous characteristic reinforces the convergence speed by reducing the search space (tightness) and simultaneously increasing the searching speed (compactness) with which solvers explore that reduced space. Uncertainty management in UC: By putting together the improvements in the previous two aspects, this thesis contributes to a better management of wind uncertainty in UC, even though these two aspects are in conflict and improving one often means harming the other. If compared with a traditional energy-block UC model under the stochastic (deterministic) paradigm, a stochastic (deterministic) ramp-based UC model: 1) leads to more economic operation, due to a better and more detailed system representation, while 2) being solved significantly faster, because the core of the model is built upon simultaneously tight and compact MIP formulations. To further improve the uncertainty management in the proposed ramp-based UC, we extend the formulation to a network-constrained UC with robust reserve modelling. Based on robust optimization insights, the UC solution guarantees feasibility for any realization of the uncertain wind production, within the considered uncertainty ranges. This final model remains as a pure linear MIP problem whose size does not depend on the uncertainty representation, thus avoiding the inherent computational complications of the stochastic and robust UCs commonly found in the literature.

Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem

Unit commitment is becoming a complex problem with the increasing constraints due to the restructuring of power system and the escalation in inclusion of various types of Distributed Generation sources. These sources offer a significantly lower generation compared to the conventional sources. Also they pose synchronising problems with the grid since the electricity cannot be transmitted over long distances and thus they provide the localized consumption of energy. In this paper, unit commitment is performed with optimal spinning reserve allocation and the assessment of reliability in terms of loss of load, in the presence of solar integration into the power system. The ‘Loss Of Load Probability’ (LOLP) index is utilized for determining the level of reliability of the obtained results. The Spinning Reserve (SR) considered in the UC calculations, is a constant value and it is not varied with respect to the changes in solar generation. Here, the spinning reserve optimality is determined with respect to the changes in power injection due to the solar energy integration into the power system. Dynamic programming technique is applied on two systems (four generator and ten generator systems) and the results are compared with those obtained without the consideration of LOLP, SR optimality and solar energy sources.

Reliability evaluation of stand-alone hybrid photovoltaic energy system for rural healthcare centre

This paper describes a methodology based on safety factor concept and peak load considerations for a reliability evaluation of a generation system. This paper also refers to major research papers presented for the evaluation of reliability of power systems, reliability indices and modelling of power system components. Load models are considered as a straight line and a stepped load duration curve is used for reliability evaluation of system using Simpson's 1/3rd rule. The probability of load exceeding the generating capacity has also been considered in Loss of Load Probability (LOLP) evaluation.

Implementing an applicable demand response (DR) program enables the complete demand-side potentials and ensures a secure, more economic and greener operation of the power systems with the integration of renewable energy. Therefore, the present paper proposes a stochastic security-constraint scheduling approach for optimum operation of both supply and demand sides via well-designed pricing and incentive schemes. The DR programs are time-of-use and emergency DR programs. The study addresses the Independent System Operator (ISO)'s viewpoint, and it aims at finding the optimal DR strategy (from a set of DR programs) in a way that an efficient electricity market is obtained, ensuring the security and environmental constraints. To this end, a security constraint unit commitment (SCUC) problem considering DR and renewable energy resources is proposed. Different indices are considered through a multi-objective problem for evaluating the efficiency of the market, security of the system, reliability and air pollution. These indices include market prices, social welfare, load factor (peak-to-valley proportion), air pollution, and power security, among others. In order to find the best DR strategy, a multi-objective problem is solved to consider all the mentioned indices.

This work deals with assessing the reliability of wind energy sources incorporated into a microgrid system to increase grid stability. As the share of power generated from wind energy continues to increase due to environmental concerns and to reduce carbon footprints, the case study proposed is designing an effective wind energy system for a smart building environment to reduce the burden on the grid. Incorporating wind energy systems into grid-connected buildings increases the reliability of the microgrid system for smart building environments. Applying smart techniques with integrated automation in the built environment can play a key role in decreasing peak demand for load development in smart cities. This can also enhance the overall efficiency of power grids, reduce waste, and enable the effective use of energy resources. Furthermore, researchers are finding new ways to collect energy from renewable sources and power-built environments due to dwindling fossil fuel resources and increased environmental awareness. The analysis of wind-connected power systems has been initiated to satisfy the increasing demand for reliable supply systems by consumers. This has prompted the use of wind energy systems with controlled techniques to enhance the reliability of power systems and reduce carbon footprints generated by fossil fuel-based power systems. Therefore, the main purpose of this work is to understand the economic prospects of wind energy sources for smart building environments, reliability analysis of wind energy sources-based power systems, reduce carbon footprints, and balance power demand through wind energy systems. The Progressive Wind Model Taylor-Sequence Proliferation (PWMTSP) and Innovative Six Parameters Based Progressive Beta Creative Model (ISPBPBCM) approaches are proposed to understand the reliability of wind energy-based power systems in smart building environments. The work also provides an economic analysis of the proposed low-cost wind power system with different capacities for residential consumers in selected geographic location. Eight cases are considered for the proposed effective wind energy system for smart building environments ranging from 2 MW to 32 MW. Hence, the economic analysis of the proposed systems is discussed for residential users. All eight cases are eco-friendly and reduce carbon emissions as these generated units from wind energy sources need not be purchased from fossil fuel-based plants, which increases the reliability of the proposed system. The expected energy not supplied (EENS) in the planned work increases as wind power capacity increases. Loss of load expectation (LOLE), loss of load probability (LOLP), and failure rate all decrease with the installation of more wind power systems. Moreover, it can be seen that mean time to repair (MTTR) improves when wind energy capacity rises incrementally from 2 MW to 32 MW. The proposed method is anticipated to be adaptable to various types of wind energy systems where dependability is a crucial component of system design and scalable to large wind integrated power systems.

This paper proposes to evaluate reliability performances at operating stage for power systems with variable wind generation. This is a first step towards recognizing the stronger coupling of power system decision making across different timescales. In the day-ahead energy-reserve co-optimization unit commitment model, the validity of using operating reserve as an approximation of long-term reliability requirement is tested with realistic wind generation profile. Reliability indices such as Loss of Load Probability (LOLP) and Loss of Expected Energy (LOEE) are computed at each hour of the day at the conclusion of the day-ahead unit commitment decision. Numerical experiments conducted in the IEEE Reliability Test System suggest that while operating reserve requirement does not change within a day, the actual reliability performance of the system varies significantly depending on (i) system loading conditions , (ii) wind power variation, and (iii) operating rules. Also, the operating reserve requirement is shown to either over-approximate or under-approximate the reliability requirement of power system. It indicates that operating reserve could be a very coarse deterministic approximation of system reliability requirement especially with high wind penetration. A much more integrated approach for modeling, analysis, and decision making is envisaged for provision of reliable and cost-effective electricity services.

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach. >

This report summarizes the current status of proposals to form Independent System Operators (ISOs) to operate high-voltage transmission systems in the United States and reviews their potential role in maintaining bulk power system reliability. As background information, the likely new industry structure, nature of deregulated markets, and institutional framework for bulk power system reliability are reviewed. The report identifies issues related to the formation of ISOs and their roles in markets and in reliability, and describes potential policy directions for encouraging the formation of effective ISOs and ensuring bulk system reliability. Two appendices are provided, which address: (1) system operation arrangements in other countries, and (2) summaries of regional U.S. ISO proposals.

In traditional electricity market unit commitment (UC) is done only for the reduction of operating cost. In the deregulated electricity market, for a secure and economical hourly generation schedule of the day ahead market, independent system operator (ISO) executes the security constrained unit commitment (SCUC) problem. The objective of the paper is to develop scheduling algorithm using hybrid particle swarm optimization (PSO) for electric generation that accounts directly for system security requirements. The proposed SCUC formulation includes constraints, such as hourly power demand, system reserves, ramp up/down limits, minimum ON/OFF duration limits. The innovation of our approach is to capture the system security requirements within a network flow sub-problem that is solved in conjunction with the unit commitment problem. To solve such SCUC model, a binary coded particle swarm optimization (BPSO) is used for unit commitment master problem and real coded particle swarm optimization (RPSO) is used for the economic dispatch sub-problem. The effectiveness of the suggested method to solve the SCUC problem is validated on different test systems.

Integration of renewable energy sources, such as wind power and solar power, into the generation system can enhance country's energy security. These alternative energy help diversify sources of primary energy used to produce electricity. However, the biggest disadvantage of using these types of renewable energy power plants is that it might reduce power system reliability because these intermittent renewable energy sources have low dependable capacity. This paper aims to evaluate the dependable capacity of wind power and solar power generation systems. It considers uncertainties due to intermittent wind speed, solar irradiance, ambient temperature, and unavailability of their corresponding generators. Additionally, load uncertainty is also taken into account. The dependable capacity of the wind power and solar power generation systems are determined from the principle of generation system reliability evaluation. The reliability index such as Loss of Load Probability (LOLP) will be used as a key indicator to define the dependable capacity.