Stochastic Unit Commitment Research Articles

Statistical Bus Ranking for Flexible Robust Unit Commitment

As the level of uncertain renewable capacity increases on power systems worldwide, industrial and academic researchers alike are seeking a scalable, transparent, and effective approach to unit commitment under uncertainty. This paper presents a statistical ranking methodology that allows adaptive robust stochastic unit commitment using a modular structure, with much-needed flexibility. Specifically, this paper describes a bus ranking methodology that identifies the most critical buses based on a worst-case metric. An important innovation is the ability to identify alternative metrics on which to rank the uncertainty set—for example, to minimize economic dispatch cost or ramping needs, to provide a customized robust unit commitment solution. Compared to traditional robust unit commitment models, the proposed model combines statistical tools with analytical framework of power system networks. The resulting formulation is easily implementable and customizable to the needs of the system operator. The method and its applications are validated against other established approaches, showing equivalent solution to the state-of-the-art approach. Case studies were conducted on the IEEE-30, IEEE-118, and the pegase-1354 networks. In addition, the flexibility of bus ranking formulation is illustrated through implementation of alternative definitions of worst-case metrics. Results show that the bus ranking method performs as well as the best of these methods, with the provision of additional flexibility and potential for parallelization.

Operation of a High Renewable Penetrated Power System With CSP Plants: A Look-Ahead Stochastic Unit Commitment Model

The integration of variable renewable energy (VRE) generation, i.e., wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch ability through the use of thermal energy storage and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: first, exploring how CSP plants operate in high renewable penetrated power systems; and second, analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method.

Scheduling isolated power systems considering electric vehicles and primary frequency response

The incorporation of renewable energy sources in isolated power systems is being significantly slower than in well-connected power systems. The intermittency and uncertainty of the power output of most renewable power technologies prevent a greater usage of these technologies in isolated power systems, in which the supply security is the major concern. In this paper we formulate a stochastic unit commitment problem that allows the participation of electric vehicles in energy, reserve capacity and primary frequency response markets in order to increase the flexibility of the power system operation. We explicitly consider the uncertainty in the power demand and renewable power availability, as well as accounting for the possibilities of contingencies of generating units using a N-1 security criterion. The proposed formulation is tested on an actual isolated power system comprising 38 generating units and 8 buses.

Stochastic Unit Commitment Incorporating Demand Side Management and Optimal Storage Capacity

High penetration of wind energy imposes several operational challenges due to its uncertainty and intermittent nature. Flexible energy resources represent key solutions to compensate for power mismatch associated with wind power (WP) uncertainty and intermittency. This paper proposes a new stochastic unit commitment (SUC) problem formulation including high penetration of wind energy, energy storage system (ESS), and demand side management. Firstly, the Latin hypercube sampling is combined with Cholesky decomposition method to generate different WP scenarios. The simulated scenarios are then reduced using the fast forward selection algorithm. Finally, a novel SUC formulation implements these reduced scenarios to size the ESS optimally, considering its cost and benefit maximization of wind energy. To validate the proposed approach, a nine-unit test system is used to demonstrate the reduction in the operational cost and the increase in the utilized wind energy under different operational conditions.

An Efficient Forecasting-Optimization Scheme for the Intraday Unit Commitment Process Under Significant Wind and Solar Power

Due to their uncertain and variable nature, the large-scale integration of wind and solar power poses significant challenges to the generator scheduling process in power systems. To support this process, system operators require using repeatedly updated forecasts of the best possible quality for renewable power. Motivated by this, the present work aims to study the benefits of incorporating spatiotemporal dependence and seasonalities into probabilistic forecasts for the intraday unit commitment (UC) process. With this purpose, a highly efficient forecasting-optimization scheme is proposed, which is composed of a detrended periodic vector autoregressive model and a technology-clustered interval UC model. The proposed approach is tested on a 120-GW power system with 210 conventional generators using real wind and solar measurements and compared to existing deterministic and stochastic UC techniques alongside standard forecasting methods. Extensive computational experiments show that the incorporation of spatiotemporal dependence and seasonalities into forecasts translates in a reduction of up to 1.55% in operational costs for a daily UC relative to standard practice, the application of intraday instead of daily UC runs further reduces operational costs in up to 1.51%, and the proposed forecasting-optimization scheme takes less than 10 h to simulate a whole year.

Congestion Risk-Averse Stochastic Unit Commitment with Transmission Reserves in Wind-Thermal Power Systems

The great proliferation of wind power generation has brought about great challenges to power system operations. To mitigate the ramifications of wind power uncertainty on operational reliability, predictive scheduling of generation and transmission resources is required in the day-ahead and real-time markets. In this regard, this paper presents a risk-averse stochastic unit commitment model that incorporates transmission reserves to flexibly manage uncertainty-induced congestion. In this two-settlement market framework, the key statistical features of line flows are extracted using a high-dimensional probabilistic collocation method in the real-time dispatch, for which the spatial correlation between wind farms is also considered. These features are then used to quantify transmission reserve requirements in the transmission constraints at the day-ahead stage. Comparative studies on the IEEE 57-bus system demonstrate that the proposed method outperforms the conventional unit commitment (UC) to enhance the system reliability with wind power integration while leading to more cost-effective operations.

Flexible line ratings in stochastic unit commitment for power systems with large-scale renewable generation

The thermal ratings of overhead transmission lines are typically conservative, which leads to underutilization of transmission assets. In this paper, we propose an optimization model that accounts for the inherent flexibility in line ratings of thermal restricted transmission lines. We determine, in a stochastic unit commitment framework, when and which line can and should adopt higher ratings (calculated based on anticipated weather conditions and loading) as part of the recourse actions. Such recourse decisions in the second stage models the capability of the transmission system to provide flexibility to mitigate the variability of renewable generation. Flexible line ratings in the recourse help improve first-stage commitment decisions. Numerical tests conducted on both IEEE 118 system and a network representing the Central European System demonstrate that with flexible line ratings recourse, the expected operation cost can be substantially reduced without degrading reliability.

Stochastic Unit Commitment and Optimal Allocation of Reserves: A Hybrid Decomposition Approach

The unit commitment is still a widely studied problem, especially when more renewable energy of stochastic character is being added to power systems. This paper proposes a model for weekly planning of systems involving hydro, wind, and thermal energy under a stochastic perspective. The proposed model follows the endogenous reserve determination criterion to achieve a simultaneous optimization of energy and reserves considering wind uncertainty, forced outages of equipment, a dc Flow model of the network, and cascaded head sensitive hydro systems, among others. The paper also develops a practical solution methodology based on outer approximation and benders decomposition. Testing has been conducted over four systems, and computational results demonstrate that the proposed model and solution are useful and effective.

Role and Benefits of Flexible Thermostatically Controlled Loads in Future Low-Carbon Systems

Thermostatically controlled loads (TCLs) represent a valuable source of flexibility for the system. Depending on network needs, these devices could alter their nominal energy consumption and provide multiple ancillary services, facilitating the cost-effective transition to a low-carbon power system. Previous work mainly focused on investigating single service provision from TCLs, while intersections among different services have not been considered. Furthermore, the intrinsic energy payback effect was not fully included within optimisation tools for TCL scheduling. This paper presents a novel demand side response model (DSRM), which enables the optimal scheduling of energy/power consumption of a heterogeneous population of TCLs and the simultaneous allocation of multiple ancillary services. The model explicitly considers the effect of the energy recovery after delivering the services so that the deliverability of scheduled services from TCLs is always guaranteed. The proposed DSRM is integrated into an advanced stochastic unit commitment model to investigate the system benefits of the flexibility from TCLs. Case studies demonstrate that: 1) time-varying provision of multiple services from TCLs significantly increases their benefits; 2) TCL operation which aims to minimise the amplitude of the energy recovery causes sub-optimal utilisation of the devices; and 3) ignoring the energy payback leads to overestimate the TCL value.

Scenario Map Based Stochastic Unit Commitment

In the power system stochastic unit commitment (SUC) model, the uncertainty and variability of the renewable energy generation, such as wind power, are usually expressed by multiple scenarios. The performance of SUC is driven by how well the selected scenarios represent the stochastic nature, since too few scenarios may lose or distort the uncertainty or variability characteristics, while too many scenarios usually cause computational difficulty. This paper proposes a novel scenario representation method referred to as scenario mapping technology (SMT), which is able to compact large amount of scenarios while preserving the uncertainty and variability features of wind power as much as possible. Specifically, SMT proposes to combine similar wind power generation into a state at each period and contains the possible multiple transitions linking the states in adjacent periods. A novel two-stage SUC model is proposed based on SMT. The numerical results demonstrate a solid improvement of the performance for the SMT-based SUC model on both cost-efficiency and calculation efficiency, compared with traditional two-stage SUC model based on the scenario reduction technique.

The impact of energy storage modeling in coordination with wind farm and thermal units on security and reliability in a stochastic unit commitment

The environmental concerns and restrictions of fossil fuel resources have led to the deployment of wind power installation. Due to the uncertain nature of wind speed, high penetration of wind power may threaten system reliability and security. Energy storage systems (ESSs) are considered to be a viable solution to this problem. In this study, a stochastic security constrained unit commitment (SCUC) problem is solved in the presence of wind and ESS units. The main concern of this paper is to study the impact of the proposed ESS model on the security and reliability of the system. To assess the reliability of a power system, the expected unserved energy (EUE) for line outage and demand underestimation contingencies is evaluated. The SCUC problem is solved by a scenario-based method incorporated with the Benders decomposition technique to mitigate congestions from power lines and provide feasibility and optimality of the solution in all the scenarios. The performance of the proposed approach has been evaluated using the 6-bus and 39-bus standard power systems. Simulation results demonstrated the economic advantages of the proposed model, while the security constraints are satisfied. Moreover, the impact of energy storage modeling on the EUE in post-contingency circumstances is discussed.

Hybrid Robust/Stochastic Unit Commitment With Iterative Partitions of the Continuous Uncertainty Set

As an attempt to reduce reliance on fossil fuels to satisfy electricity demand, the penetration of renewable sources like wind or solar power has experienced rapid growth in recent years. Due to their intermittent nature, the exact contribution of renewable sources to electricity supply is at least partly unknown. In order to be able to accommodate this uncertainty in real-time, sufficient capacities in the form of thermal and hydro power plants must be available. On that account, power system operators solve the so-called unit commitment problem after bidding in the day-ahead market closes to derive schedules for their power plants which can ensure system reliability at low costs. In literature, two approaches have mainly been studied in this context: Robust and stochastic unit commitment. Being a worst-case formulation, robust unit commitment puts its focus on reliability. A common drawback of this approach is that it tends to deliver over-conservative schedules. Stochastic unit commitment on the other hand creates more cost-effective schedules by preparing for the expected case, but either fails to guarantee system reliability or puts a high workload on the CPU. The limitations of known formulations have sparked interest in so-called hybrid approaches, which aim at combining the ideas of robust and stochastic unit commitment in a favorable way. This paper reports a novel hybrid approach to solve the unit commitment under uncertainty, which yields both robust and cost-efficient schedules. The new method respects the continuous nature of uncertainties and is thus in particular favorable for applications in power systems with high penetration of volatile renewable sources. By merging the ideas of robust and stochastic unit commitment, the proposed hybrid formulation minimizes the expected worst-case dispatch costs. Our method relies on partitioning the continuous range of the uncertainties into subsets. By means of the number of partitions, the solution can be adjusted between the conservative robust and the cost-efficient stochastic unit commitment in a user-friendly manner. A Benders decomposition algorithm is derived to solve the hybrid unit commitment efficiently. Finally, a case study confirms the superior performance of the proposed method.

A novel priority-based stochastic unit commitment considering renewable energy sources and parking lot cooperation

Environmental concerns have caused application of renewable energy sources as a clean appropriated alternative for conventional thermal generators in power system planning. These uncertain and variable sources have challenged the power system scheduling to ensure that it still remains reliable and economical. Also, plug-in electric vehicles may potentially alter load demand in uncontrolled charging mode and impose new challenges to the unit commitment problem. This paper demonstrates the fast heuristic method based on priority list to solve the stochastic unit commitment problem and applied it to a basic 10-unit case study system accompaniment with an electric vehicle parking lot, a wind farm and a solar farm over a 24-h time horizon. As it is reported, scenario generation with Monte Carlo simulation can relatively compensate for the intermittent behavior of renewable energy sources and result in more economical and robust planning. Moreover, the integration of plug-in electric vehicles and their controlled charging/discharging is considered. Thermal units are scheduled by priority list method with their relevant constraints such as minimum up/down time, spinning reserve, load demand and capacity limitation. The simulations results verify that the penetration of the mentioned renewable energy sources can improve the operational cost and computation time, effectively.

A Dynamic Multistage Stochastic Unit Commitment Formulation for Intraday Markets

As net-load becomes less predictable there is a lot of pressure in changing decision models for power markets such that they account explicitly for future scenarios in making commitment decisions. This paper proposes to make commitment decisions with multiple gate closures. Our proposed model also leverages a state-space formulation for the commitment variables, through which the operational constraints of generation units participating in the market are respected. We also study the problem of constructing scenario tree approximations for stochastic processes and evaluate our algorithms on scenario tree libraries derived from real net-load data.

Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method

In light of the dissemination of renewable energy connected to the power grid, it has become necessary to consider the uncertainty in the generation of renewable energy as a unit commitment (UC) problem. A methodology for solving the UC problem is presented by considering various uncertainties, which are assumed to have a normal distribution, by using a Monte Carlo simulation. Based on the constructed scenarios for load, wind, solar, and generator outages, a combination of scenarios is found that meets the reserve requirement to secure the power balance of the power grid. In those scenarios, the uncertainty integration method (UIM) identifies the best combination by minimizing the additional reserve requirements caused by the uncertainty of power sources. An integration process for uncertainties is formulated for stochastic unit commitment (SUC) problems and optimized by the improved genetic algorithm (IGA). The IGA is composed of five procedures and finds the optimal combination of unit status at the scheduled time, based on the determined source data. According to the number of unit systems, the IGA demonstrates better performance than the other optimization methods by applying reserve repairing and an approximation process. To account for the result of the proposed method, various UC strategies are tested with a modified 24-h UC test system and compared.

Stochastic Unit Commitment With Topology Control Recourse for Power Systems With Large-Scale Renewable Integration

In this paper, we model topology control through transmission switching as a recourse action in the day-ahead operation of power systems with large-scale renewable generation resources. We prove that transmission switching could only reduce the linear objective value of direct current optimal power flow when congestion exists. However, we also show, through a simple example, that unit commitment cost could be reduced by transmission switching even in the absence of congestion. To solve the stochastic unit commitment with topology control within reasonable computational time, we proposed a heuristic that first decomposes a practical system into zones and then solves the problem for each zone in parallel. The benefit of topology control recourse is demonstrated on a network representing the Central European System. We compare the costs of the network with different loading and renewable generation conditions. The cost reduction of the test system can reach 3.34% with heavy load and large-scale renewable generation while in a single zone the cost reduction can be above 7%.

Stochastic Unit Commitment Based on Multi-Scenario Tree Method Considering Uncertainty

With the increasing penetration of renewable energy, it is difficult to schedule unit commitment (UC) in a power system because of the uncertainty associated with various factors. In this paper, a new solution procedure based on a multi-scenario tree method (MSTM) is presented and applied to the proposed stochastic UC problem. In this process, the initial input data of load and wind power are modeled as different levels using the mean absolute percentage error (MAPE). The load and wind scenarios are generated using Monte Carlo simulation (MCS) that considers forecasting errors. These multiple scenarios are applied in the MSTM for solving the stochastic UC problem, including not only the load and wind power uncertainties, but also sudden outages of the thermal unit. When the UC problem has been formulated, the simulation is conducted for 24-h period by using the short-term UC model, and the operating costs and additional reserve requirements are thus obtained. The effectiveness of the proposed solution approach is demonstrated through a case study based on a modified IEEE-118 bus test system.

Storage management by rolling stochastic unit commitment for high renewable energy penetration

This paper presents a unified unit commitment and economic dispatch model that integrates storage devices for the short-term operations scheduling of power systems with high renewable penetration. The presented model is a single multi-hour look-ahead real-time tool that uses multiple time resolution to contain computational requirements. The decisions for the first time interval are binding while the decisions of the remaining scheduling horizon are advisory. Adopting this approach, storage facilities are more efficiently utilized, by constantly adapting their energy injection/withdrawal schedule based on updated system information, thus, alleviating the problem of defining the appropriate stored energy level during economic dispatch. The proposed model is presented in both deterministic and stochastic frameworks. The operational impacts of storage and the benefits of implementing stochastic optimization are validated via extensive simulations using data from the Greek Interconnected Power System.

Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment

With the increasing penetration of wind power and demand response integrated into the grid, the combined uncertainties from wind power and demand response have been a challenging concern for system operators. It is necessary to develop an approach to accommodate the combined uncertainties in the source side and load side. In this paper, the fuzzy stochastic conditional value-at-risk criterions are proposed as the risk measure of the combination of both wind power uncertainty and demand response uncertainty. To improve the computational tractability without sacrificing the accuracy, the fuzzy stochastic chance-constrained goal programming is proposed to transfer the fuzzy stochastic conditional value-at-risk to a deterministic equivalent. The operational risk of forecast error under fuzzy stochastic conditional value-at-risk assessment is represented by the shortage of reserve resource, which can be further divided into the load-shedding risk and the wind curtailment risk. To identify different priority levels for the different objective functions, the three-stage day-ahead unit commitment model is proposed through preemptive goal programming, in which the reliability requirement has the priority over the economic operation. Finally, a case simulation is performed on the IEEE 39-bus system to verify the effectiveness and efficiency of the proposed model.

Assessment of Future Whole-System Value of Large-Scale Pumped Storage Plants in Europe

This paper analyses the impacts and benefits of the pumped storage plant (PSP) and its upgrade to variable speed on generation and transmission capacity requirements, capital costs, system operating costs and carbon emissions in the future European electricity system. The combination of a deterministic system planning tool, Whole-electricity System Investment Model (WeSIM), and a stochastic system operation optimisation tool, Advanced Stochastic Unit Commitment (ASUC), is used to analyse the whole-system value of PSP technology and to quantify the impact of European balancing market integration and other competing flexible technologies on the value of the PSP. Case studies on the Pan-European system demonstrate that PSPs can reduce the total system cost by up to €13 billion per annum by 2050 in a scenario with a high share of renewables. Upgrading the PSP to variable-speed drive enhances its long-term benefits by 10–20%. On the other hand, balancing market integration across Europe may potentially reduce the overall value of the variable-speed PSP, although the effect can vary across different European regions. The results also suggest that large-scale deployment of demand-side response (DSR) leads to a significant reduction in the value of PSPs, while the value of PSPs increases by circa 18% when the total European interconnection capacity is halved. The benefit of PSPs in reducing emissions is relatively negligible by 2030 but constitutes around 6–10% of total annual carbon emissions from the European power sector by 2050.