Generation Unit Commitment Research Articles

Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints

The significant increase in solar PV and wind capacity worldwide has raised growing concerns about power system frequency stability in several countries. One reason for this is that these renewable technologies offer almost no inertial response. However, there are some options available to address this issue. Renewables could now offer virtual inertia. Also, power system operators could impose frequency stability constraints as part of the generation Unit Commitment (UC). These options could potentially have a large impact on renewable generation and hence also on energy costs and system emissions. In this paper, we study two key options improving stability, inertia from solar PV generators supplied by virtual synchronous machines, and frequency stability constraints as part of the UC. We assess N-1 generation contingencies for the interconnected power systems of Chile, Argentina, and Peru. We found that the option of virtual inertia reduces the violation of the RoCoF in hours of high solar generation, without affecting renewable generation. On the other hand, frequency stability constraints ensure non-violation of all stability parameters but reduce renewable generation by 12 %. This reduction increases total operating costs by about 35 %, even when the constraints are combined with virtual inertia and battery support.

Mixed Integer Nonlinear Programming-Based Unit Commitment of Conventional Thermal Generators Using Hybrid Evolutionary Algorithms

Unit Commitment (UC) discusses the optimized generation resources (to turn on economical generators and turn off expensive generators),which are subjected to satisfy all the operational constraints. The operational constraints such as load balancing, security maximization, minimum up and down time, spinning reserve, and ramp up and down constraints are difficult to satisfy. Although, UC is a cost minimization problem that is realized by committing less expensive units while satisfying the corresponding constraints, and dispatching the committed units economically. The UC problem is an np-hard Mixed Integer Nonlinear Problem (MINLP). Therefore, in this paper, hybrid EA based on a Genetic Algorithm (GA) has been applied to find the optimal solution to the UC problem. Moreover, during the search process, it is very difficult to discard infeasible solutions in EAs. Hence, the Genetic Algorithm (GA) is integrated with the feasibility rule constraint handling technique to emphasize feasible solutions. IEEE RTS Eleven Thermal Generator Standard Test system is used to validate the performance of proposed methods. For the validation and the superiority of the proposed algorithm, simulation results are compared with the classical Lagrangian Relaxation (LR) methods. Results show that the proposed method can find the global optimal solution to the UC problem which is subjected to satisfy all the operational constraints.

A tri-level Typhoon-DAD robust optimization framework to enhance distribution network resilience

Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon “Mangkhut” (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources.

Impact of short-term wind forecast accuracy on the performance of decarbonising wholesale electricity markets

As the proportion of variable renewable generation capacity in our electricity systems rises, so too will the associated absolute forecast errors in their generation. This work quantifies the complex interactions between short-term renewable generation forecasts, short-term demand forecasts, generator unit commitment and economic dispatch in electricity systems with levels of variable renewable generation spanning 49–79% of annual electricity demand. When the short-term operational uncertainty caused by renewable generation becomes larger in absolute terms than that of demand, the accuracy of renewable generation forecasting has a critical influence on electricity market performance. That is, inaccurate short-term wind forecasts can increase hours of unserved energy and scarcity hours spent at market price cap, increase total system costs and prices, significantly increase returns to thermal generators and energy storage, reduce returns to wind generators, and increase greenhouse gas emissions slightly. For the generalised case study considered in this work, the value of improved short term wind generation forecasts looks to becomes significant once variable renewables generate more than roughly 60% of annual electricity demand. Reductions in average market prices of 5 $/MWh or more then appear plausible as acceptable levels of reliability are recovered. As such, improvements in short-term renewable generation forecasting are likely required in order to justify continued investment in variable renewables as the major means of achieving decarbonisation efficiently and reliably, and these could be achieved by any combination of improved forecasting, dispatch control or on-site firming.

Optimisation of generation unit commitment and network topology with the dynamic thermal rating system considering N-1 reliability

The electrical grid is a vital component of modern society that requires significant and cost-effective investments to ensure its reliability. The dynamic thermal rating system (DTR) and network topology optimization (NTO) aim to increase the capacity of existing networks and provide flexible options for enhancing power system reliability. The co-optimization framework for generation unit commitment and network switching is formulated with for N-1 reliability, a gap that has not been previously addressed. This is important as it ensures: (1) economic efficiency in power system despatch, (2) optimal operation of both generation and transmission systems, and (3) power grid operation that can withstand the loss of any single component without causing a cascading failure. The reliability impacts of these new proposed operating strategies are also tested on complex, large-scale power systems under long-term, multi-area weather conditions. This proposed work is useful for power system planners, as it helps them identify ways to improve the reliability and efficiency of power systems under various weather conditions. This paper shows that using the DTR-NTO combination improves EENS by 50% in both test cases compared to the original condition. Additionally, it also makes the reliability assessment more realistic when taking into account multi-area weather conditions.

GENERATION UNIT COMMITMENT MIXED INTEGER LINEAR MODEL FOR SIMULTANEOUS HEAT AND ELECTRIC DAILY LOAD COVERING

The unit commitment problem nowadays is widely used in the electric power sector. The problem was first time formulated in the 1940-s and still developing both methodologically and by including an additional number of technologies each of which has a different unique mathematical treatment corresponding to the specific technology's behavior. The common characteristic of the problem such as that is dedicated to the electricity production sector, hence the mathematical formulation is following pure electricity sector transformation but during the last years the Power-to-X technologies are implemented and their further development is expected in the future. This requires the advancement or at least modification of the problem formulation to meet possible exchange and usage between different types of energy within one integrated power system. The goal of the article is to further development of the existing versions of the unit commitment problem, which are dedicated to the operation of the generation in the power system by implementing additional equations allowing contemplation of the heat energy-producing technologies which are dedicated to cover a heat-energy load of the district heating systems. This should allow for conducting comprehensive studies of the simultaneous operation of electric- and heat-generating technologies to meet the energy demand of local energy systems, which is important for designing distributed generation mix, for example at a municipal level. The proposed mixed integer linear generation unit commitment model for simultaneous heat and electric daily load covering is described in the article. The proposed model in addition to the pure electric power balance also meets heat load using only-heat technologies (fuel boilers), combined heat and power units, and also industrial-scale electric boilers - which are converting electricity to heat energy. Keywords: mixed integer linear model, unit commitment problem, integrated power system, electric boilers, power-to-X technologies, conventional electricity generating technologies.

Real‐time supply‐demand schedule update and operation for generators and battery energy storage system based on forecasted and actual photovoltaic power outputs

AbstractThe photovoltaic (PV) power output might be frequently curtailed to maintain electricity supply‐demand balance in future power systems. In our previous study, we proposed a new method for updating the battery energy storage system (BESS) charge/discharge and the generator unit commitment (UC) schedules based on the forecasted and actual PV power outputs. The forecast dataset was updated every 3 h (eight times a day). Although the simulation results showed that the proposed method could reduce the supply‐demand imbalances, it was not clear whether the forecasted or actual values made contributions. Therefore, in this study, we propose and evaluate a real‐time scheduling and operation method using the forecasted and actual PV power outputs assuming that the forecasted dataset is updated only once a day. Numerical simulations of supply‐demand operations are conducted on the power system model of the Kanto area of Japan for one year. The results show that the previous study method has a slight advantage over proposed method in terms of curtailed PV energy and operational cost of thermal generators reduction, but the difference is very small, indicating that the contribution of the actual PV power outputs is greater than that of the forecasted PV power outputs.

RETRACTED: Optimal unit commitment integrated energy storage system, renewable energy sources and FACTS devices with robust method

This study investigated the unit commitment (UC) programming in the presence of both wind energy and energy storage sources. Programming this problem provides an optimal timetable for generation unit commitment to maximize security and minimize costs, as well as meet the grid and unit constraints in a period, as a major research topic. Today, the relationship between energy storage units and the grid has received considerable attention of researchers and grid operators. As the flexible alternating current transmission system (FACTS) devices promote the security of transfer lines, the UC problem was solved in the presence of FACTS devices in this paper. The salient feature of the proposed model is the no requirement of predicting uncertain variables and parameters; instead, uncertain parameters were defined in a “certain interval”. Then, these intervals were sequentially divided into smaller intervals, so that a set of robust integer programming problems can be defined and solved more easily. Another feature of the robust optimization method is to obtain the possible and optimal solution for the worst-case scenario for uncertain parameters in the defined uncertainty interval. By solving the proposed model, sufficient information was created directly for forming the units’ hourly bidding curves. The proposed model was discussed in different scenarios and the results indicated its proper performance.

Unit commitment optimization of a micro-grid with a MILP algorithm: Role of the emissions, bio-fuels and power generation technology

Management strategies of complex energy systems composed by different technologies is mandatory to exploit optimally the characteristics of each power generator, to reduce the cost of energy, the impact of greenhouse gases emissions and to increase the penetration of mini- and micro-grids into energy systems. To this purpose, optimization methods and algorithms have to be developed to assess the unit commitment of generators and to suggest decision variables in the definition of the emission costs. In this paper, a novel Mixed Integer Linear Programming (MILP) optimization algorithm has been developed to compute the optimal management of a micro-energy grid composed either by four Internal Combustion Generators (ICGs), or three ICGs and a Micro Gas Turbine (MGT). The algorithm optimizes a multi objective function that takes in consideration the total cost, the NOx and the CO2 emissions of the system, while setting some technological constraints, like start-ups and transients that are typically neglected. Moreover, different fuelling of the devices is evaluated. The model proved the importance of including an accurate model of the greenhouse gases emissions as they can significantly affect the optimization results. Furthermore, it proved to be very flexible and to be a proper basis to be adopted in more complex systems embedding energy storage devices and renewable energy systems.

Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

Stochastic Optimization for Security-Constrained Day-Ahead Operational Planning Under PV Production Uncertainties: Reduction Analysis of Operating Economic Costs and Carbon Emissions

This paper presents a general operational planning framework for controllable generators, one day ahead, under uncertain re-newable energy generation. The effect of photovoltaic (PV) power generation uncertainty on operating decisions is examined by incorporating expected possible uncertainties into a two-stage unit commitment optimization. The planning objective consists in minimizing operating costs and/or equivalent carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) emissions. Based on distributions of forecasting errors of the net demand, a LOLP-based risk assessment method is proposed to determine an appropriate amount of operating reserve (OR) for each time step of the next day. Then, in a first stage, a deterministic optimization within a mixed-integer linear programming (MILP) method generates the unit commitment of controllable generators with the day-ahead PV and load demand prediction and the prescribed OR requirement. In a second stage, possible future forecasting uncertainties are considered. Hence, a stochastic operational planning is optimized in order to commit enough flexible generators to handle unexpected deviations from predic-tions. The proposed methodology is implemented for a local energy community. Results regarding the available operating reserve, operating costs and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions are established and compared. About 15% of economic operating costs and environmental costs are saved, compared to a deterministic generation planning while ensuring the targeted security level.

Robust online scheduling for optimal short-term operation of cascaded hydropower systems under uncertainty

The uncertainties in system and model parameters arising from the volatility of market, weather and operating conditions pose a major challenge to the optimal short-term operation of cascaded hydropower systems. The dynamic operating environment resulting from the fluctuating parameters greatly impacts the scheduling of power generation and generating unit commitment in such systems. This article focuses on the development and implementation a novel rolling horizon robust online scheduling framework that utilizes stochastic optimization within a model-based feedback scheme to tackle the uncertainties in electricity prices, electric power demands, water inflows and plant model parameters. The efficacy of this approach is demonstrated through application to a variety of case studies for different types of uncertainty. Case studies demonstrate significant improvements in system performance with the proposed strategy, in terms of system economics and constraint satisfaction, over schedules generated without feedback or use of a nominal online scheduling scheme.

A Genetic Based Algorithm for Short-Term Thermal Generating Unit Commitment.(Dept.E)

Unit Commitment is one of the most complex and difficult optimization problem in power systems. The objective of the optimal commitment is to determine the on/off states of the units in the system to meet the load demand and spinning reserve requirements at each time period, such that the overall cost of generation is minimized, while satisfying the various operational constraints. This paper presents the application of an improved genetic algorithm (GA) with crossover, mutation, and advanced operators such as repair and swap mutation to determine the commitment order of the thermal units in power generation. The proposed GA has been successfully applied to 10 and 26 generating-unit systems. Robustness of the proposed GA is demonstrated by comparison to the dynamic programming algorithm. The comparison results show the effectiveness of the proposed GA in solving the unit commitment problem with sufficient accuracy and low computational time.

Day-ahead load forecasting using improved grey Verhulst model

Purpose In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model. Design/methodology/approach To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019. Findings The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent. Practical implications In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters. Social implications In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages. Originality/value This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

Co-optimization of Transmission Maintenance Scheduling and Production Cost Minimization

Regular transmission maintenance is important to keep the infrastructure resilient and reliable. Delays providing on-time maintenance increase the forced outage rate of those assets, causing unexpected changes in the operating conditions and even catastrophic consequences, such as local blackouts. The current process of maintenance schedule is based on the transmission owners’ choice, with the final decision of system operator about the reliability. The requests are examined on a first-come, first-served basis, which means a regular maintenance request may be rejected, delaying the tasks that should be performed. To incorporate optimization knowledge into the transmission maintenance schedule, this study focuses on the co-optimization of maintenance scheduling and the production cost minimization. The mathematical model co-optimizes generation unit commitment and line maintenance scheduling while maintaining N-1 reliability criterion. Three case studies focusing on reliability, renewable energy delivery, and service efficiency are conducted leading up to 4% production cost savings as compared to the business-as-usual approach.

The long-term impacts of carbon and variable renewable energy policies on electricity markets

We present a computationally-efficient optimization model that finds the least-cost generation unit expansion, commitment, and dispatch plan to serve hourly electricity demand and ancillary service requirements. We apply the model to a case study based on data from the electricity market in Texas (ERCOT) to analyze the market and investment impacts of several incentive mechanisms that support variable renewable energy (VRE) investments and carbon emission reductions. In contrast to many previous studies, the model determines least-cost VRE investments under different cost and incentive assumptions rather than analyzing scenarios where VRE expansion is pre-determined. We find that electricity prices can vary significantly under different incentive mechanisms, even when comparable generation portfolios result. Therefore, the preferred incentive mechanism depends on stakeholder objectives as well as the prevailing electricity market framework. Our results indicate that a carbon tax is more system cost-efficient for reducing emissions, while production and investment tax credits are more system cost-efficient for increasing VRE investments. Similarly, incentive mechanisms that reduce electricity prices may increase the need for separate revenue sufficiency mechanisms (e.g. a capacity market) more than a policy that increases electricity prices. Moreover, the impacts on consumer payments are not always aligned with changes in system costs. Overall, the analysis illustrates the importance of considering electricity market impacts in assessing the economic efficiency of VRE and carbon incentive mechanisms.

Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost.

Coordinated operation of a battery energy storage system and thermal generators for supply–demand balance maintenance and efficient use of photovoltaic energy

AbstractIn future power systems with extremely large integration of photovoltaic (PV) generation, it is necessary to not only properly maintain supply–demand balance without power shortfall but also utilize as much PV energy as possible. The application of PV power forecast and energy storage devices to power system operation is necessary. In this study, we propose a method for determining and modifying the charging and discharging schedule of a battery energy storage system (BESS) and the unit commitment of thermal generators based on the PV power forecast. The proposed method is evaluated by numerical simulations for one year. The results show that both the energy shortfall and PV curtailment can be reduced by the proposed method.

A multi-stage hybrid artificial intelligence based optimal solution for energy storage integrated mixed generation unit commitment problem

A multi-stage hybrid artificial intelligence based optimal solution for energy storage integrated mixed generation unit commitment problem

Two-Stage Optimization of Battery Energy Storage Capacity to Decrease Wind Power Curtailment in Grid-Connected Wind Farms

As wind power makes an increasing contribution to power systems, the problems associated with wind power curtailment have become a concern in recent years. Battery energy storage (BES) can reduce the effects of wind power curtailment by peak shaving and wind power forecast error compensation. Accordingly, the operational constraints of power systems and wind power uncertainty should be considered in the optimization of BES capacity installed at wind farms. This paper proposes a two-stage method to determine the optimal power and capacity of BES in systems including thermal plants, wind farms, and BES. In the first stage, the unit commitment of thermal generators and scheduled wind farm output are optimized with ac power flow constraints modeled by second-order cone programming. Time series of the wind farm output generated by Monte Carlo simulations are used for BES optimization. In the second stage, operational strategies for BES are designed. The simulation results indicate that cooperation between the BES and wind farm using the proposed method can reduce the costs of both wind farms and thermal plants. Finally, a sensitivity analysis is performed to assess the influence of the BES unit cost and wind power penetration on the optimal power and capacity of BES.