Unit commitment considering coordinated dispatch of large scale electric vehicles and wind power generation

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.

Based on the study of uncertainty and fluctuation of wind power, a novel algorithm including bad operation scenarios set and robust optimization method was proposed in the paper trying to solve probabilistic unit commitment problem of power system which has wind power generation. According to the power system probabilistic dispatch theory, the conservative indicator to evaluate the value of deterioration for bad scenarios set is established, and the variance is added in the optimization target to suppress some individual bad scenarios which may lead to deterioration of the overall optimization results. Furthermore, energy storage system, which has an inherent ability to mitigate active power fluctuation, was taken into account to contribute to the presented unit commitment models. Finally, the numerical example which includes cost, storage capacity, robustness metric and conservatism degree analysis verifies the effectiveness and practicality of the proposed methods.

Wind farm outputs have the features of intermittence and variability which impose a significant impact on the operation of power systems. In this paper, Latin hypercube sampling (LHS) and reduction technique is used to simulate the 24h power output of a wind farm. Then a model of unit commitment (UC) with predicted wind power (UCW) is established, and a harmony search (HS) with arithmetic crossover operation (ACHS) is employed for solving this problem. The results are analyzed in detail, which assess the impact of wind power on UC and demonstrate that ACHS is practicable for UCW problem with comparison with other proposed HS methods.

This paper describes development of a unit commitment model with demand response and electric storage device for the integration of variable renewable energies by considering forecast outputs and errors for photovoltaic and wind power generations. We analyze operation plans for power systems in Tokyo area and Tohoku area where a large amount of variable renewable energies are introduced in 2030. We estimate curtailment of wind power for Tohoku area in 2030 because the power system in Tohoku area can not integrate wind power generation planned to be installed in 2030. Then this paper shows that the integrated operation of the two power systems can integrate both photovoltaic and wind power generations planned to be installed in 2030. Finally we evaluate economic efficiency for the integrated operation of the two power systems.

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and cost-efficiently allocate the required reserves. The current state-of-the-art stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a state-of-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

The integration of wind resource into the electric grid brings significant challenges due to the variable nature and anti-peak-regulation characteristic of wind power. Based on least square method, an improved normal distribution model is proposed to fit the actual wind power forcast error. Furthermore, considering wind power forecast error and the great potential of battery energy storage system (BESS) technology to mitigate the impact of volatile wind power, a unit commitment (UC) model with large capacity BESS has been estanbished in this study. Case studies with modified IEEE 39-bus system are employed to validate the proposed method. The role of BESS on economics, peak load shifting and accommodating wind power is discussed. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4937

Due to the stochastic and fluctuated nature of wind energy, it is difficult to predict wind power accurately. Thus, the day-ahead optimal dispatching for power system integrated large-scale wind power has a significant challenge. Therefore, a unit commitment (UC) method considering stochastic wind power is proposed to mitigate the impact of inaccurate predicted error in this paper. Firstly, the improved Lagrange relaxation algorithm is used to obtain the dual problem of the generator unit model. Secondly, based on the prediction of wind power interval, the scenario method is used to deal with the fluctuation of wind power through the scene generation and improved fast backward reduction technology. Finally, the 10-unit system model is built, and the feasibility of the proposed UC model is proved by comparing the stochastic UC model and deterministic model.

Unit commitment (UC) is a problem to economically determine the commitment status and generation level for each thermal unit during each time interval of the scheduled periods. The purpose of UC is to minimize the total generation cost while satisfying system demand, reserve requirements, and other unit constraints. The combined-cycled units have been increasingly installed throughout the world because of their high efficiency and fast response. However, the combined-cycle units usually have multiple operating configurations. The multi-configuration characteristic causes that the UC problem is more complex and difficult to be solved. In this paper, the authors present a simplified combined-cycled unit model to efficiently solve the related mixed integer linear programming-based UC problem. By simulating two test systems, results show that the proposed model is effective to reduce the complexity of problem with losing little solution accuracy.

In this study, a heuristics‐based optimisation methodology for a day‐ahead unit commitment (UC) model in microgrids is proposed. The model aims to schedule the power among the different microgrid units while minimising the operating costs together with the CO2 emissions produced. A storage device is added where the charge and discharge schedule is calculated according to both objectives. In addition, as a part of the demand side participation strategy, a charging schedule was determined for the electric vehicles (EV) in order to increase the system security and further reduce the costs and emissions. A congestion management approach is also introduced, which eliminates congestions by effective unit scheduling according to congestion signals provided by the distribution system operators. The complete day‐ahead time horizon is divided in 96 time steps (each with a 15 min time span), which makes the UC problem more complicated. The studied system includes renewable energy resources, a storage unit, two microturbines, a fuel cell and EVs. The results demonstrate that the proposed model is robust and is able to reduce the microgrid operating costs and emissions by optimal scheduling of the microgrid units, and is able to take into account local congestion problems.

The paper proposes a stochastic unit commitment (UC) model to realize the low-carbon operation requirement and cope with wind power prediction errors for power systems with intensive wind power and carbon capture power plant (CCPP). A linear re-dispatch strategy is introduced to compensate the wind power deviation from the spot forecast. The robust optimization technique is employed to obtain a reliable commitment plan against all realizations of wind power within the uncertainty set given by probabilistic forecast. The proposed model is validated with IEEE 39-bus system. The advantages of flexible CCPPs are compared to the normal coal-fueled plants and the impacts of robustness controlling are discussed.

Many EU countries have used financial incentives to promote electric light commercial vehicles and decarbonise urban freight transport. However, the high costs of electric light commercial vehicles still pose a significant adoption barrier. This paper evaluates the impact of financial incentives on the total cost of ownership of electric light commercial vehicles in the European Union and their role in achieving the cost-competitiveness of electric vehicles. A pairwise comparison is performed for three weight categories of electric and diesel vehicles in twelve countries. Our analysis shows that financial incentives have a high impact on the total cost of ownership and have proven necessary to achieve electric vehicles' cost-competitiveness in most cases. However, in specific cases, financial incentives may no longer be needed as the cost of electric vehicles' ownership would have been lower than diesel vehicles even without them. This paper demonstrates the importance of adjusting government incentives to avoid unnecessary funding allocations while promoting the decarbonisation of light commercial vehicles, particularly as the number of incentives does not directly correlate with electric vehicles penetration.

Most analyses of the future European energy system conclude that in order to achieve energy and climate change policy goals it will be necessary to ramp up the use of renewable energy sources. The stochastic nature of those energies, together with other sources of shortand long-term uncertainty, already have significant impacts in current energy systems operation and planning, and it is expected that future energy systems will be forced to become increasingly flexible in order to cope with these challenges. Therefore, policy makers need to consider issues such as the effects of intermittent energy sources on the reliability and adequacy of the energy system, the impacts of rules governing the curtailment or storage of energy, or how much backup dispatchable capacity may be required to guarantee that energy demand is safely met. Many of these questions are typically addressed by detailed models of the electric power sector with a high level of technological and temporal resolution. This report describes one of such models developed by the JRC's Institute for Energy and Transport: Dispa-SET 2.0, a unit commitment and dispatch model of the European power system aimed at representing with a high level of detail the short-term operation of large-scale power systems. The new model is an updated version of Dispa-SET 1.0, in use at the JRC since 2009. Table of

The power system inertia is gradually decreasing with the growing share of variable renewable energy (VRE). This may jeopardize the frequency dynamics and challenges the secure operation of power systems. In this paper, the concept of frequency security margin is proposed to quantify the system frequency regulation ability under contingency. It is defined as the maximum power imbalance that the system can tolerate while keeping frequency within the tolerable frequency range. A frequency constrained unit commitment (FCUC) model considering frequency securitymargin is proposed. Firstly, the analytical formulation of system frequency nadir is derived while considering both the frequency regulation characteristics of the thermal generators and the frequency support from VRE plants. Then, the frequency security margin is analytically formulated and piecewise linearized. A novel FCUC model is proposed by incorporating linear frequency security constraints into the traditional unit commitment model. Case studies on a modified IEEE RTS-79 system and HRP-38 system are provided to verify the effectiveness of the proposed FCUC model. The impacts of VRE penetration on system frequency security are analyzed using frequency security margin.

When a large-scale random charging load is connected to the regional power grid, it can negatively affect the safe and stable operation of the power grid. Therefore, we need to study its charging load and response potential in advance so that electric vehicles can interact well with the grid after being connected to the regional grid. Firstly, after analyzing the influencing factors of regional electric vehicle ownership, an electric vehicle ownership prediction model based on the sparrow search algorithm-improved BP neural network (SSA-BPNN) is established. On this basis, an electric vehicle charging load prediction model is established based on the sparrow search algorithm-improved BP neural network and Monte Carlo algorithm (SSA-BPNN-MC). Secondly, the charging behavior of different types of electric vehicles is analyzed and modeled, and the data from a certain area are taken as an example for the prediction. Then, according to the load forecasting results, the potential of electric vehicles participating in demand response in the region in the future is deeply analyzed using the scenario analysis method. Finally, with the aim of resolving the problems of massive multi-source heterogeneous data processing and the management of electric vehicles participating in the regional power grid demand response, a basic framework of electric vehicles participating in the regional power grid demand response is developed, which provides effective support for promoting electric vehicles to participate in regional grid demand response.

Considering wind farm integrations, a robust optimization model for security-constrained unit commitment (SCUC) is built to deal with the errors on wind power predictions. In the proposed formulation, spinning reserve requirement is specially considered to support possible wind power change between two successive time iterations. Network security constraints are considered by DC power flow equations. Moreover, an iterative method is proposed to solve the SCUC problem considering network losses by using loss factors and loss distribution factors. Case studies on the IEEE 30-bus system demonstrate the effectiveness of the proposed method. The results under different wind integration scenarios are also compared.