Reliability Constrained Day Ahead Unit Commitment with Optimal Spinning Reserve Allocation for Solar Integrated Power System

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.

The strategies involved in scheduling generators and reliability management of power system is changing rapidly due to restructuring of power system. This paper critically reviews the reliability impacts of solar integrated thermal power plants and DR management. DR refers to the actions taken by the Independent system operator (ISO) to respond to a shortage of supply for a short duration of time, thereby maintaining the system reliability by scheduling spinning reserves. This paper presents a Binary real coded firefly (BRCFF) algorithm for solving Security constrained unit commitment (SCUC) problem for the hybrid power system incorporating reliability constraints. The Loss of load probability (LOLP) is the reliability index used for evaluating the system reliability level. The evaluation of the reliability index includes both unavailability of thermal generator units and unavailability of Solar cell generators (SCG). The proposed methodology is tested and validated on an IEEE RTS 24 bus system. (8 pages)

The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.

Green bus stations use solar energy to operate the station, a notable innovation in the public transportation sector. The station is capable of providing its own energy source due to the solar power and energy storage systems. The solar energy source is exploited by an innovative maximum power point tracking (MPPT) method that can optimally exploit the available solar energy source. The improved MPPT method exhibits a fast convergence time in all solar intensity conditions. The fast convergence time ranges from 0.54-0.64 seconds. The bus station is integrated with other amenities—such as fans, smart lighting systems or smartphone chargers—to create comfort for passengers. A station model was built to encourage people to use public transportation, contributing to the goal of global environmental protection.

The principle of power determined by heat and the anti-peak-regulation characteristics of wind power output cause the difficulty of peak regulation in the power grid along with the production of the abandon wind. By analyzing the topological differences between power system and thermal system, establishing positive and negative spinning reserve constraints of the district heating network, and considering both the hot and the cold start-up cost of the units as well as the thermoelectric coupling of the CHP units, a unit commitment model of wind power accommodation in combined heat and power system— whose objective function is to minimize the total coal consumption and environmental cost of the units—is presented in this paper. In this model, unit commitment and optimized dispatch are combined, the electric load is shared by all the units, and the heat load is balanced by the local CHP plants respectively. Finally, the optimization results show that while meeting power and heat load demand, the model of this paper can timely start up or shut down units according to load change and spinning reserve demand, reduce total minimum technical output of operating units, and further improve wind power accommodation rate.

Guest Editorial: Situational awareness of integrated energy systems

Optimal planning for wind power capacity in an electric power system

Day-ahead thermal and renewable power generation scheduling considering uncertainty

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

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.

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.

Minimum energy storage (ES) and spinning reserve (SR) for day-ahead power system scheduling with high wind power penetration is significant for system operations. A chance-constrained energy storage optimization model based on unit commitment and considering the stochastic nature of both the wind power and load demand is proposed. To solve this proposed chance-constrained model, it is first converted into a deterministic-constrained model using p-efficient point theory. A single stochastic net load variable is developed to represent the stochastic characteristics of both the wind power and load demand for convenient use with the p-efficient point theory. A probability distribution function for netload forecast error is obtained via the Kernel estimation method. The proposed model is applied to a wind-thermal-storage combined power system. A set of extreme scenarios is chosen to validate the effectiveness of the proposed model and method. The results indicate that the scheduled energy storage can effectively compensate for the net load forecast error, and the increasing wind power penetration does not necessarily require a linear increase in energy storage.

Wind and solar as two renewable energy resources are largely used to generate clean and sustainable energy in the power systems. To integrate these renewable energies in the power system, different aspects of the power system such as reliability and operation are affected that must be investigated. It is due to the variation in the generated power of these resources that are arisen from the variation in the wind speed and solar radiation. To model the uncertainty nature of large-scale wind and photovoltaic farms in the operation studies of the power system, an appropriate multistate reliability model is developed for the wind and photovoltaic farms considering both failure of main components and variation in the wind speed and solar radiation. To determine the optimum number of states in the reliability model of wind and photovoltaic farms, XB index is calculated and based on the fuzzy c-means clustering technique the transition rates among different states are obtained. To calculate the transition rates among different states, the fuzzy numbers are used that results in the accurate and reduced reliability model for wind and photovoltaic farms. To determine the probability of different states in the reliability model of wind and photovoltaic farms in the operation studies, matrix multiplication technique is utilized to determine important indices of the power system such as the unit commitment risk. In this paper, the amount of required spinning reserve of a power system containing wind and solar generation units can be determined based on the reliability criterion. The proposed technique is applied to the two reliability test systems including RBTS and IEEE-RTS and the reliability-based operation indices of these systems are calculated to present the effectiveness of the proposed analytical method.

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.

Renewable energy sources (RESs), such as wind and solar energy, are an integral part of the current process of decarbonisation of electric power systems in the vast majority of countries. Due to the stochastic and variable nature of these resources, RESs could expose an electrical network to unexpected power outages. In this context, security studies are vital for the secure and day-to-day operation of the infrastructure. This article analyses the effects of integrating renewable energies into a power system from the perspectives of reliability and robustness. For this purpose, the IEEE 14-bus test system is modified, and two case studies are proposed with a high proportion of fossil-based power generation and a high share of renewable generation, respectively. In both cases, hourly load and electricity generation profiles are considered. The reliability study is completed using the Monte Carlo probabilistic method, and the robustness study is conducted by simulating cascading failures. These assessments are carried out in the two scenarios, where the electricity system without renewable energies is considered the base case. The effects of renewable energies are analysed comparatively in terms of Loss of Load Probability (LOLP), Expected Demand Not Supplied (EDNS) and Satisfied Demand (SD).