Generation Scheduling Model Research Articles

Optimization of short-term hydropower scheduling with dynamic reservoir capacity based on improved genetic algorithm and parallel computing

The optimal scheduling of hydropower plants is important for the efficient operation and management of hydropower energy systems. In the short-term optimization scheduling of river-type reservoirs, the dynamic reservoir capacity is a key factor. Currently, the short-term power generation scheduling model considering dynamic reservoir capacity was solved by the enumeration method, providing a more accurate simulation of the water flow process compared to the model considering static reservoir capacity. However, the solving process had the disadvantage of a large computational burden and excessive time consumption, which restricted application in the actual scheduling of reservoirs. Therefore, to solve the time-consuming problem of repeated calculation of hydrodynamics model in optimization scheduling, an improved genetic algorithm including adaptive and elitist-selection strategies combined with the parallel computing technique was proposed. The optimal scheduling plan generated by this model were compared with the optimization results of the enumeration method and the genetic algorithm scheduling model. The results showed that the optimal power generation selected by the three models were extremely similar and the optimization model could find the optimal value for the scheduling period. In terms of computing speed, the improved genetic algorithm parallel technique took approximately 0.8 h, around 246 times faster than the enumeration method and 20 times faster than the model without using parallel computing. It demonstrates that the present algorithm is practically usable, significantly enhancing the optimization efficiency of scheduling solutions. Furthermore, it satisfies the engineering application efficiency requirements and can provide technical support for the subsequent power generation scheduling of the Three Gorges Reservoir.

Research on Power System Day-Ahead Generation Scheduling Method Considering Combined Operation of Wind Power and Pumped Storage Power Station

In the proposed wind-storage combined operation technology, the storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on the operational characteristics of pumped storage power stations, the day-ahead dispatching method of a power system with wind farms and pumped storage power stations is studied. The dispatching mode that aims at the lowest operating cost is proposed, taking into consideration the coordination relationship between the scheduling benefit of pumped storage power stations and the total peak-shaving economy of the system and the fluctuation of new energy output. First, taking the constraint of reservoir capacity, the output power, and the daily pumping power of the pumped storage power station into account, a day-ahead generation scheduling model is constructed, with the objective of minimizing costs. Then, the imperial competition algorithm is applied to the proposed model. Finally, the algorithm is compared with the standard particle swarm optimization algorithm. The simulation results based on standard 4-unit and 10-unit systems indicate that the proposed method is effective and robust for a power system with wind power and pumped storage power stations.

Feasibility of Behind-the-Meter Battery Storage in Wind Farms Operating on Small Islands

This paper investigates the anticipated benefits from the introduction of a battery energy storage system (BESS) behind-the-meter (BtM) of a wind farm (WF) located in a small non-interconnected island (NII) system. Contrary to the standard storage deployment applications for NII, where storage is either installed in front of the meter as a system asset or integrated into a virtual power plant with renewable energy sources, the BESS of this paper is utilized to manage the power injection constraints imposed on the WF, aiming to minimize wind energy curtailments and improve WF’s yield. A mixed integer linear programming generation scheduling model is used to simulate the operation of the system and determine the permissible wind energy absorption margin. Then, a self-dispatch algorithm is employed for the operation of the WF–BESS facility, using the BESS to manage excess wind generation that cannot be directly delivered to the grid. Additionally, the contribution of BESS to the capacity adequacy of the NII system is investigated using a Monte Carlo-based probabilistic model, amended appropriately to incorporate storage. Finally, an economic feasibility analysis is carried out, considering the possible revenue streams. By examining several BESS configurations, it has been shown that BtM BESS reduces energy curtailments and contributes substantially to resource adequacy as its energy capacity increases. However, the investment feasibility is only ensured if the capacity value of the BtM storage is properly monetized or additional dependability of wind production is claimed on the ground that the inherent intermittency of the wind production is mitigated owing to storage.

Evaluating effects of battery storage on day-ahead generation scheduling of large hydro–wind–photovoltaic complementary systems

The complementary management of hydro, wind, and photovoltaic (PV) energy can effectively promote new energy consumption. However, the flexibility of hydropower is often inhibited by comprehensive water use in the river basin, which impairs the complementary performance of the system. Integrating battery storage into a hydro–wind–PV (HWP) complementary system is promising for enhancing the system’s flexibility, but it is unclear whether and how much battery storage can improve the complementary system. This paper proposes a methodology for evaluating the effects of battery storage on the system’s complementarity. First, a day-ahead generation scheduling model was established for HWP and hydro–wind–PV–battery (HWPB) complementary systems, and a two-layer nested framework is used to solve these models. Then, a fuzzy decision evaluation method is applied to quantify the improvements of battery storage on complementary performance. Finally, intraday joint operation strategies are proposed for the battery storage and cascade hydropower plants, and the influences of battery storage on the hydropower efficiency are discussed. Results using the clean energy base in the upper Yellow River basin indicated that: (1) battery storage with 10% capacity configuration ratio can improve the complementary performance of the system by 23.09%; (2) the operational risks are reduced by 98.18%, and the peak shaving performance and daily average delivered power are increased by 3.98% and 0.55%, respectively; and (3) the joint operation may reduce the hydropower efficiency in spring, summer, and autumn, setting the planned charge time period at a later valley load period can reduce or avoid this negative effect; however, the joint operation can improve the hydropower efficiency in winter, which can be further increased by setting the planned discharge time period at a later peak load period. Generally, battery storage can significantly improve the complementary performance of HWP systems, and the proposed joint operation strategies are effective in guiding the short-term operation of HWPB systems.

Chance-constrained co-optimization for day-ahead generation and reserve scheduling of cascade hydropower–variable renewable energy hybrid systems

The rapid development of variable renewable energy (VRE), such as wind and solar energy, has stimulated the complementary operation of VRE with flexible cascade hydropower stations in China. Due to the uncertainties of VRE power generation and complex constraints of cascade hydropower stations, formulating reliable day-ahead generation and reserve scheduling plans is a real challenge in the actual operation stage of the cascade hydropower–VRE hybrid systems (CHVHS). In this paper, we propose a tractable chance-constrained co-optimization model for day-ahead generation and reserve scheduling of a CHVHES. First, compared with existing models, the relationship between the hydropower reserve capacities and water-electricity conversion efficiency is finely modeled. Accordingly, the economic allocation of total VRE reserve requirements among cascade hydropower stations is for the first time considered in our proposed model. This can save more water resources for cascade hydropower stations when compensating for VRE, further improving hydro–VRE complementary profits. Second, we propose a solution approach for chance constraints by incorporating the nonparametric probabilistic forecasting of VRE power based on quantile regression into the chance-constrained model, ensuring that the stochastic dependence between VRE power output and its point forecast can be effectively captured. Importantly, this solution approach does not require prior knowledge or any probability distribution assumptions of VRE power and does not introduce any additional computational burden. With the help of three-dimensional interpolation technology for nonlinear constraints, the proposed scheduling model is finally cast as a mixed-integer linear programming model that is computationally tractable. Numerical tests implemented on a real CHVHES located in Southwest China verify the effectiveness and advantages of the proposed methods.

Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids

Photovoltaic (PV) generation can drop sharply under reductions in solar irradiance due to cloud cover, resulting in adverse impacts on the performance of stand-alone microgrids. In addition, due to the stochastic nature of solar irradiance, it has remained challenging to forecast PV generation accurately. Therefore, a sufficient amount of operating reserve must be allocated for the variability and uncertainty of PV generation. However, excess operating reserve can decrease the economic efficiency of microgrids. This paper proposes an optimal generation scheduling and operating reserve management scheme for stand-alone PV-integrated microgrids, which comprises three core models. First, a two-stage recurrent neural network-based model is proposed for solar irradiance and cloud cover forecasting. Second, the operating reserve required for PV is managed more effectively based on weather conditions. Finally, an optimal generation scheduling model is applied based on the forecasting and operating reserve management models. The proposed scheme is applied to representative case studies to validate its advantages. The forecasting accuracy of the proposed model outperforms a conventional model. Under clear-sky conditions, the operating reserve for PV can be reduced down to 30% of PV output. Subsequently, the optimal operation scheduled by the proposed scheme can produce substantial savings of more than 5.5%.

Solution of short term integrated hydrothermal-solar-wind scheduling using sine cosine algorithm

The combined scheduling of hydro, thermal and hybrid energy (i.e., wind and solar) is attracting more and more attention nowadays. This paper explores the contribution of renewable energy sources in the traditional hydrothermal scheduling problem in order to overcome the prevailing issues like high generation cost and more emissive particles of fuel. This paper presents a new wind-solar based generation scheduling model that reduces the aforementioned issues. The proposed hydrothermal-solar-wind scheduling (HTSWS) model is very effective. It can reduce the price rate of generation and different emissive particles of fuel of the traditional scheduling model. A solution approach i.e., robust sine cosine algorithm (SCA) is employed to determine the minimum generation cost and fuel emission based best generating schedule for traditional scheduling problem. The same approach has been carried out in the context of the suggested HTSWS problems. The different control parameters of the SCA have been properly utilized to balance the exploration and the exploitation phases. These parameters lead to determination of the near-optimal global solution. The results show that the combined application of renewable sources and hydrothermal units benefit both economic and emission goals of the utility houses with operating costs and pollutant emissions falling by 10% and 64%, respectively. Here, another two powerful algorithms (i.e., backtracking search algorithm and whale algorithm) have also been applied to determine the minimum values of generation cost, emission, and combined generation cost-emission. Effectiveness and robustness of the proposed algorithm have been presented by comparing the SCA based results with other established latest algorithms for the considered problems.

More Environmental Sustainability Routing and Energy Management for All Electric Ships

With the popularity of the electrification of marine transportation, strategic energy-saving and environment-friendly management is gaining more attention recently. This paper proposes a novel coordinated navigation routing and power generation scheduling model, which aims at making a compromise between investment cost, operation cost, and greenhouse gas emissions under the distributional robust ambiguity of photovoltaic. A maritime hybrid energy configuration that combines diesel generator (DG), battery energy storage system (BESS), fuel cell (FC), photovoltaic (PV), and the cold-ironing connection is presented with a real-world navigation routine from Dalian to Singapore, and the optimization problem is solved through a bi-level tri-objective differential evolution algorithm, where navigation parameters, ESS and FC capacity and weight between operation cost and emission functions, are optimized in the upper level and specific power generation scheduling is settled in the lower level. Six case studies are conducted to verify its effectiveness and accuracy, and the simulation results demonstrate the proposed method can further reduce the operation cost while minimizing air contamination.

Risk-averse day-ahead generation scheduling of hydro–wind–photovoltaic complementary systems considering the steady requirement of power delivery

Optimizing day-ahead generation schedules of hydro–wind–photovoltaic (PV) complementary systems (HWPCSs) can help to promote the accommodation of wind and solar energies. However, it is challenging to formulate appropriate generation schedules for the large HWPCS that contains cascade hydropower plants, in particular, a steady requirement of power delivery is considered in the optimization model. To further improve complementary performance of the large HWPCS, we propose a risk-averse day-ahead generation scheduling approach that considers the steady requirement of power delivery. First, a representative scenario set is used to characterize forecast uncertainties of the wind and PV power. Then, a multi-objective optimal generation scheduling model with consideration of the operational risks of electricity curtailment and power shortage is proposed. Finally, a two-layer nested optimization framework is designed to derive the system’s generation schedule. The clean energy base in the upper Yellow River basin, China was selected as a case study. The results show that: (1) forecast uncertainties of wind and PV power are more likely to induce power shortage risk in summer and autumn, but to induce electricity curtailment risk in spring and winter; (2) without using extra constraint handling strategies, the proposed approach could directly yield a stair-shaped power delivery curve, which is good for long-distance power transmission applications; and (3) compared with a traditional method without considering the operational risks, the proposed generation scheduling approach could significantly reduce the comprehensive risk rate by 65% on average, while the cascade hydropower production and peak shaving performance are satisfactory. Therefore, the proposed approach is effective in guiding the day-ahead generation scheduling of the HWPCSs that contain cascade hydropower plants.

Short-term optimal scheduling of cascade hydropower plants shaving peak load for multiple power grids

Cascade hydropower plants which have good regulation performance and are managed by the dispatching center of regional power grids are usually required to simultaneously shave the peak load for multiple provincial power grids, which is an important way of relieving the growing peak shaving pressure on power grids in central and eastern China. This paper establishes an optimization model for the short-term generation scheduling of cascade hydropower plants in regional power grids. In this model, minimizing the peak-valley load difference of multiple power grids is adopted as the objective function. In addition to conventional hydraulic constraints, the operation constraints of individual hydropower units, electrical constraints and head effect on the power generation are all considered to obtain precise scheduling. The original nonlinear and non-convex model is converted into a standard mixed-integer linear programming (MILP) formulation through several linearization strategies. The results for the real-world case study indicate that: 1) the proposed model is computationally efficient with a calculation time of 326 s; 2) the peak-valley differences of the Shanghai Power Grid and Zhejiang Power Grid decreased by 9.71% and 2.29%, respectively; 3) compared with actual operation, the proposed model shows better performance in peak shaving for multiple provincial power grids.

Decentralized monthly generation scheduling of cascade hydropower plants in multiple time scale markets

Centralized optimization is widely used in generation scheduling of cascade hydropower plants to make full use of reservoir capacity. However, it is very common that the upstream and downstream plants of cascade hydropower system belong to different operators. Therefore, the problems of information privacy and profit distribution are inevitable, which prevents the application of centralized optimization. In order to solve these problems, a decentralized monthly generation scheduling model of cascade hydropower plants in multiple time scale markets is presented in this paper. First, the decoupling method of cascade hydropower plants is introduced. Then a flow-based bilateral contract is designed. The upstream and downstream plants can sign contract to increase their revenues, thereby increasing total revenues of cascade hydropower system. Finally, the hydropower scheduling problem is modelled based on limited information exchange to protect information privacy. The problem is solved by alternating direction method of multipliers (ADMM). The feasibility and effectiveness of the presented model and methods are verified by case studies.

Battery Energy Storage Contribution to System Adequacy

The objective of this paper is to evaluate the contribution of energy storage systems to resource adequacy of power systems experiencing increased levels of renewables penetration. To this end, a coherent methodology for the assessment of system capacity adequacy and the calculation of energy storage capacity value is presented, utilizing the Monte Carlo technique. The main focus is on short-duration storage, mainly battery energy storage systems (BESS), whose capacity values are determined for different power and energy configurations. Alternative operating policies (OPs) are implemented, prioritizing system cost or reliability, to demonstrate the significant effect storage management may have on its contribution to system adequacy. A medium-sized island system is used as a study case, applying a mixed integer linear programming (MILP) generation scheduling model to simulate BESS and system operation under each OP, in order to determine capacity contribution and overall performance in terms of renewable energy sources (RES) penetration, system operating cost and BESS lifetime expectancy. This study reveals that BESS contribution to system adequacy can be significant (capacity credit values up to ~85%), with energy capacity proving to be the most significant parameter. Energy storage may at the same time enhance system reliability, reduce generation cost and support RES integration, provided that it is appropriately managed; a combined reliability-oriented and cost-driven management approach is shown to yield optimal results.

Application of flexible ramping products with allocation rates in microgrid utilizing electric vehicles

Microgrids account for a relatively high proportion of renewable energy utilization. Therefore, an operation method that ensures flexible resources to cope with the variability and uncertainty in renewable energy is indispensable for microgrid operators (MGOs). Here, we focus on the necessity of an optimal generation scheduling model for MGOs by considering flexible resources, namely flexible ramping products (FRPs). We propose a microgrid scheduling model including a method of utilizing electric vehicles (EVs) as a flexible resources and an operation method to secure FRPs in microgrids by applying allocation indices. The states and available capacities of EVs during each period are estimated using a Markov chain. The net load scenarios, which are considered as the target for the supply–demand balancing in the proposed method, are generated considering the uncertainty in forecasting load and renewable energy. The effectiveness and merits of the proposed model are validated through experiments on a microgrid test system that allows transactions between utilities through the tie-line. The results confirm that the proposed optimal generation scheduling model affords a reduction in microgrid operating costs and enables the stabilization of variability.

Evaluating the cost of emissions in a pool-based electricity market

This paper proposes a methodology that quantifies the impact of emissions cost on the electricity price, the generation scheduling outcome, and the overall emissions in a pool-based electricity market. It employs a mid-term (yearly) generation scheduling model, by sequentially solving a Day-Ahead Scheduling problem, in which power plants internalize their emissions cost in the energy offers that they submit to the day-ahead market. It further explores several scenarios with respect to gas and carbon prices, as well as the realization of random outages that affect the availability of the generation units. An illustration on an instance of the Greek electricity market during the European Union Emissions Trading Scheme Phase III indicates that: (i) A carbon price equal to 30 €/tCO2e combined with low gas prices results in a 18.7% reduction of CO2 emissions, due to the substitution of lignite units by gas units in the energy generation mix. (ii) An increase in the carbon price by 1 €/tCO2e results in an increase of the weighted average electricity price ranging from 0.52 to 0.61 €/MWh. (iii) The average pass-through rate of the carbon costs onto the demand-side payments for carbon prices up to 15 €/tCO2e is close to 60%.

An effective energy flow management in grid-connected solar–wind-microgrid system incorporating economic and environmental generation scheduling using a meta-dynamic approach-based multiobjective flower pollination algorithm

In this research paper, we focus on developing a generation scheduling model using an intelligent soft-computing technique in a microgrid (MG) system. A multiobjective power management system with innovative features of the MG technology is presented The necessity and reason for undertaking this study is to optimize MG operation as well as address the uncertainty of random energy production from renewables by utilizing demand response (DR) programs. A meta-dynamic-approach-based multiobjective flower pollination algorithm is applied to solve this complex, nonlinear, multiobjective optimization (MOO) problem. Energy management in MGs utilizing renewable energy is a salient feature. DR schemes are conducted in residential, commercial, and industrial customers. Simulations are performed to achieve reduced prices and minimum emissions. Comparative studies were conducted wherein the metaheuristic algorithm demonstrated superior performance and higher efficiency compared to other technique. Operating costs reduced by 20.3% and emissions reduced by 5% after the implementation of DR programs using a meta-dynamic-approach-based flower pollination algorithm compared to particle swarm optimization (PSO). The results demonstrate the superiority of the proposed demand-side management modeling method.

Conditions for Regional Frequency Stability in Power System Scheduling—Part II: Application to Unit Commitment

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region.

Proposing Intelligent Energy Management Model for Implementing Price Rate in Microgrids Using Demand Response Program

The present research paper proposes a microgrid generation scheduling model and illustrates a design infrastructure for implementing price management in microgrids with demand response program using intelligent soft computing technique. Higher-order metaheuristic approach like Exponent-decreasing-inertia-weight-particle-swarm-optimization (EDW-PSO) which aids to find precision solution is applied thereby minimizing desired goal. Demand-response-(DR)-schemes are accomplished in manner of inducement remittance on household, mercantile and manufacturing customers. The work considers microgrid system model and consists of different power components like Micro-turbine-(MT), Wind-turbine-(WT), Photo-voltaic-(PV), Fuel-cell-(FC), Battery-power source and responsive loads are used. Numerical and graphical outcome attained present ascendancy in suggested demand side management modeling approach using EDW-PSO for effective generation scheduling in microgrids. Results show that prices decreased utilizing EDW-PSO compared to Particle Swarm Optimization (PSO).

Dual-timescale generation scheduling with nondeterministic flexiramp including demand response and energy storage

Increased grid-connected wind power generation results in net load with higher volatility as well as larger ramping requirements. It is becoming imperative to provide flexible ramping products (flexiramp) for better generation / load alignment and smoother wind power integration. This paper advocates a non-deterministic flexiramp modeling approach devised in a hybrid stochastic / deterministic dual-timescale framework. The latter uses a day-ahead chance-constrained scheduling formulation along with a deterministic real-time online rolling scheduling model, to facilitate the procurement of flexiramp products in the two timescales. Demand response and energy storage are implemented and quantified as additional flexiramp product. Flexiramp reserve is introduced, to ensure sufficient flexiramp capacity throughout the timescales. To reduce the wear and tear of generators and consider the operational and maintenance costs in the proposed framework, cycling ramping (cycliramp) cost is subsumed as a penalty in the day-ahead objective function. Simulations are carried out for the 24-bus and 118-bus systems. Numerical results highlight the efficacy of the proposed generation scheduling models, along with the impact of contemplating flexiramp and cycliramp cost components in the dual-timescale scheduling approach.

Challenges in the Management of Hydroelectric Generation in Power System Operations

The management of hydroelectric generation in the context of power system operations has been a difficult and important problem since the inception of power systems more than a century ago; however, various current developments are leading to important new associated challenges and opportunities: massive integration of variable renewable energy and other disruptive technologies, climate change effects on the availability of hydro inflows, and also new efficient techniques for optimization under uncertainty. Multistage stochastic optimization and stochastic dual dynamic programming are currently the dominant techniques for hydroelectric generation scheduling problems; however, there are many recent extensions and improvements on such techniques, and alternative approaches are being developed with significant potential for future concrete applications from power system operators and policy makers. In this context, this paper presents a literature review on hydroelectric generation scheduling models, and a discussion on the critical challenges, open research questions, and future lines of research associated to this problem.

Day-Ahead Contingency-Constrained Unit Commitment With Co-Optimized Post-Contingency Transmission Switching

This paper addresses the incorporation of transmission switching in the contingency-constrained unit commitment problem within the context of co-optimized electricity markets for energy and reserves. The proposed generation scheduling model differs from existing formulations due to the joint consideration of four major complicating factors. First, transmission switching actions are considered both in the pre- and post-contingency states, thereby requiring binary post-contingency variables. Secondly, generation scheduling and transmission switching actions are co-optimized. In addition, the time-coupled operation of generating units is precisely characterized. Finally, practical features of modern power systems, such as uncertain nodal net injections and the operation of energy storage, are also considered. The proposed model is cast as a challenging mixed-integer program for which the off-the-shelf software customarily used for simpler models may lead to intractability even for moderately-sized instances. In order to circumvent this computational issue, this paper presents an enhanced and novel application of an exact nested column-and-constraint generation algorithm featuring the inclusion of valid constraints to improve the overall computational performance. Numerical simulations based on the IEEE 118- and 300-bus systems demonstrate the effective performance of the proposed approach as well as its economic and operational advantages over existing models disregarding post-contingency transmission switching.