Power Generation Dispatch Research Articles

Coordinated Dispatch of Power Generation and Spinning Reserve in Power Systems with High Renewable Penetration

The spinning reserve demand in high renewable penetration power systems is increasing significantly due to the stochastic and unpredictable nature of renewable power. This paper defines the expected load not supplied ratio (ELNSR) and models uncertainty factors such as unit forced outage rates, load and wind power output prediction errors based on probability density functions. It derives a quantitative relationship between system operating reserves and the ELNSR and uses this quantitative relationship as a constraint for power generation scheduling. Based on this, a coordinated generation and reserve scheduling model for power systems with large-scale wind power is established. Case study results demonstrate that the proposed model can balance both economy and reliability, coordinate the output allocation between wind power and thermal power, and provide an optimized allocation scheme for operating reserves among thermal power units to meet corresponding reliability requirements.

Short-Term Prediction Model of Wave Energy Converter Generation Power Based on CNN-BiLSTM-DELA Integration

Wave energy is a promising source of sustainable clean energy, yet its inherent intermittency and irregularity pose challenges for stable grid integration. Accurate forecasting of wave energy power is crucial for reliable grid management. This paper introduces a novel approach that utilizes a Bidirectional Gated Recurrent Unit (BiGRU) network to fit the power matrix, effectively modeling the relationship between wave characteristics and energy output. Leveraging this fitted power matrix, the wave energy converter (WEC) output power is predicted using a model that incorporates a Convolutional Neural Network (CNN), a Bidirectional Long Short-Term Memory (BiLSTM) network, and deformable efficient local attention (DELA), thereby improving the accuracy and robustness of wave energy power prediction. The proposed method employs BiGRU to transform wave parameters into power outputs for various devices, which are subsequently processed by the CNN-BiLSTM-DELA model to forecast future generation. The results indicate that the CNN-BiLSTM-DELA model outperforms BiLSTM, CNN, BP, LSTM, CNN-BiLSTM, and GRU models, achieving the lowest mean squared error (0.0396 W) and mean absolute percentage error (3.7361%), alongside the highest R2 (98.69%), underscoring its exceptional forecasting accuracy. By enhancing power forecasting, the method facilitates effective power generation dispatch, thereby mitigating the adverse effects of randomness on the power grid.

Financial Viability of Thermal Power Generation Plants in the Transition to Renewable Energy

The energy transition will occur due to the natural decline in profitability of thermal power generation assets, and their place in the energy matrix will be taken over by renewable energies. Ensuring electric supply security, a cornerstone of the energy trilemma, is a priority for both developed and developing countries. Nations heavily reliant on hydrological resources employ thermal energy as a backup, making the exploration of financial feasibility for such projects pertinent. This study introduces an economic valuation model for a dispatchable thermal power generation plant in the Colombian market. Associated uncertainty is assessed based on revenue from market sales, bilateral contracts, and firm power remuneration – elements constituting the cash flow and converging into profitability and risk analysis. These are calculated through the Monte Carlo simulation methodology. Additionally, a sensitivity analysis is conducted, accounting for variables such as the "Reliability Payment" settlement price, capacity factor, and costs linked to coal supply. The results emphasize the need for firm power remuneration as a crucial incentive for the success of such projects. Furthermore, the criticality of dispatch level in relation to profitability is verified. Finally, the impact of coal taxes and supply negotiations on financial feasibility is assessed.

Assessment of Colombian renewable energy auctions policy: Enabler or barrier for concentrating solar power plants

Auctions are an alternative for introducing renewable energy (RE) projects into electricity markets at competitive prices. Colombia added 2.2 GW capacity of RE through this mechanism. Nevertheless, 94% of the awarded generation is allocated between 12 a.m. and 5 p.m. because of a lack of low-cost storage. Integrating concentrating solar power plants (CSP) could be relevant in tackling this issue. Therefore, a preliminary step is to determine if Colombian instruments benefit the deployment of CSP and trigger the discussion about its future in the country. In this work, we evaluate how auction mechanisms and fiscal incentives (FI) influence the Levelized Cost of Electricity (LCOE) and Internal Rate of Return (IRR) for two CSP configurations. The results indicate that FIs reduce capital expenditures between 17.11% and 19.45%, reaching similar LCOEs to those reported internationally. However, energy prices higher than USD 150/MWh are required to achieve an adequate IRR. Regarding the auction design, it is observed that a “security criterion” leads to a significant loss of competitiveness for CSP as it neglects its possibility to support demand at night. Policy suggestions are provided for a level playing field where low-cost solutions can coexist with dispatchable renewable power generation.

A Study on Hybrid Power Plant (Solar & Biomass)

We have designed an on-grid hybrid power plant that combines solar and biomass energy sources. The solar plant has a capacity of 6 MW, and the biomass plant has a capacity of 2 MW. The hybrid solar-biomass power plant is still a recent renewable technology. It is an ongoing investigation that not only proposes dispatchable power generation but also benefits from having a flexible input source, including solar and bioenergy. We aimed to successfully design a hybrid project using proper analysis, to reduce the cost of power generation through a grid-connected hybrid system, while also reducing greenhouse gas emissions. We used PVsyst software to simulate the solar plant and created the PV mounting structure and Single Line Diagram (SLD) using AutoCAD software. We have used Microsoft Excel software to carry out the cost analysis. We also calculated how much biomass waste and gas the biomass plant will need. We have been able to successfully design our hybrid power plant and, at the same time, reduce the power generation cost comparatively. The hybrid system design has made it possible to ensure the most convenient arrangement of electricity.

Dispatchable generation analysis and prediction by using machine learning: A case study of South Africa

South African power sector has a total installed capacity of about 58,095 MW. However, in the past five years, the nation's energy sector has been struggling to generate a daily average power of about 23,526 MW. Therefore, the use of machine learning technique is crucial to observing the short-term forecast of South African dispatchable power generation and its effects on the nation's economic growth. The choice of SARIMAX as the forecasting tool in this study is due to its flexibility, simplicity, accuracy, and robustness. This study has carried out time series modeling of the South African dispatchable power generation between the period: 2019 and 2023 by using the seasonal auto-regressive moving average with exogenous variables (SARIMAX). The accuracy of the model was measured by the root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). When subjected to ARIMA analysis, the predictive model produced the following metrics: RMSE of 669.05 MW, MAE of 575.07 MW, R2 value of 0.937, MBE of -2.45, and MAPE of 0.023. However, upon employing the SARIMAX method, notable improvements were observed, with the metrics indicating RMSE of 469.92 MW, MAE of 330.80 MW, R2 of 0.969, MBE of -0.09 MW, and MAPE of 0.015. The improved accuracy provided by the SARIMAX predictive method confirms the importance of considering seasonal effects in dispatchable generation prediction models. Dispatchable power generation prediction is an important route to achieving sustainable energy for a nation's development, social well-being, and security of life and properties.

Optimal sizing of hybrid PV–diesel–biomass gasification plants for electrification of off-grid communities: An efficient approach based on Benders’ decomposition

Nowadays, millions of people in remote areas do not enjoy an uninterrupted power supply due to the lack of connectivity to the main power grid. Under such circumstances, the only feasible way to access electricity is typically local power generation, often relying on diesel engine–generator sets or photovoltaic arrays. However, on many occasions, this configuration does not fully exploit all available local resources, including biomass. Indeed, most isolated areas have access to local biomass production from agricultural activities, which can be used for local electricity generation through gasification. This paper addresses this challenge by developing an innovative optimal sizing tool for hybrid power plants integrating biomass gasifiers, specifically designed for isolated areas with access to local biomass production. The novel approach models the particular features of biomass gasification technologies, including long on/off times or restrictive ramping limits. To this end, an efficient methodology based on representative weeks is proposed, which is combined with a solution strategy based on the multi-cut Benders’ decomposition, thus resulting in a tractable framework that can deal with a huge amount of data efficiently. One of the most salient features of the new proposal is the consideration of local biomass production, which is included in the methodology through an original algorithm. Accordingly, a certain amount of biomass is sourced locally, leading to more accurate and reliable results. The new methodology is applied to a benchmark off-grid community in Ghana. The results demonstrate that the use of gasifiers reduces the project cost notably (by 90%) driven by the reduced biomass cost, which can be supplemented by locally generated biomass from agricultural activities. In addition, this technology constitutes a clean source of energy, reducing the total CO2 emissions by 83% compared to a baseline case in which only diesel generators are used. Moreover, it is demonstrated that biomass gasification can effectively act as base load power generation technology to reliably cover most of the local demand, thereby enabling a clean and inexpensive dispatchable local power generation. Finally, a sensitivity analysis reveals that the economic feasibility of the plant is more sensitive to the biomass cost than the selling price of biochar, resulting in a 33% increment in the total project cost when the price of biomass increases from 0 to 0.4 $/kg. Nevertheless, gasification remains as the predominant power generation technology even under unfavorable prices.

Solution to uncertainty of renewable energy sources and peak hour demand in smart grid system

Today's need for environmental sustainability prompted the smart grid to incorporate more renewable energy sources. Reducing the operational costs of power generation and emission dispatch can both be accomplished with the integration of renewable energy sources. A power system is made to manage the fluctuating nature of loads for a brief time, nevertheless. However, new difficulties arise for utilities as a result of the fluctuation and uncertainty associated with renewable sources. Another problem faced by generating plants is network reliability issues arising from the peak hour's power demand in the power system. Application of demand response technology with the help of smart meters connected at consumers' end in smart cities can help to ease the enormous peak-hour pressure on power generating units. But a hybrid power system integrated with a storage system faces the challenge of optimal utilization of the battery under uncertainties caused by intermittent generation of power and dynamic demand. This research work proposes a real-time price based hidden Markov model and an enhanced demand response technique to solve these issues in a hybrid power system. Proposed model is tested on IEEE 30-bus system with/without renewable energy sources and implemented grasshopper optimization algorithm for optimal scheduling of generating units.

Ultimate flexibility in future CO2-free dispatchable power generation: Transition pathways for the Netherlands

With the increase in intermittent power supply by renewables, there is an integration challenge in the power system and a need for new approaches to supply-demand matching. Dispatchable capacity is expected to remain an essential source of ultimate flexibility, although active for only a few hours per year. We assess the (dis)advantages of different CO2-free dispatchable power options for the Netherlands through a literature review and expert interviews. Subsequently, we use these findings and different policy goals to project possible transition pathways. For the Netherlands, these pathways differ mainly in the short-term, but align on a hydrogen-based solution in the long-term.

Ambient data-driven participation factors related to oscillation modes based on subspace dynamic mode decomposition

Analyzing the connection between the variables participating in the oscillation and modes in electromechanical oscillation is particularly important for maintaining system stability. This paper proposes an ambient data-driven method based on Subspace Dynamic Mode Decomposition (Sub-DMD) to extract the Participation Factors (PFs) related to system state and algebraic variables in electromechanical oscillation modes. This method uses ambient data to calculate the low-dimensional approximate matrix of the system, and the PFs is extracted by using eigen-decomposition and mode energy. Compare the extracted PFs with the results of power generation dispatch. The effectiveness of the proposed method is demonstrated by using simulation data from IEEE 4-generator 2-area test system and IEEE 16-generator 68-bus test system.

Bi-level configuration and operation collaborative optimization of shared hydrogen energy storage system for a wind farm cluster

Energy storage is indispensable to achieve dispatchable and reliable power generation through renewable sources. As a kind of long-duration energy storage, hydrogen energy storage systems are expected to play a key role in supporting the net zero energy transition. However, the high cost has become an obstacle to hydrogen energy storage systems. The shared hydrogen energy storage (SHES) for multiple renewable energy power plants is an emerging mode to mitigate costs. This study presents a bi-level configuration and operation collaborative optimization model of a SHES, which applies to a wind farm cluster. Different operation modes, including the ‘electricity‑hydrogen-electricity (E-H-E)’ mode and the ‘electricity‑hydrogen (E-H)’ mode, are considered. Results show that SHES system offers substantial advantages over individual hydrogen energy storage solutions. It achieves a notable reduction in annual operation and maintenance (O&M) costs by 9.5 % and a significant increase in annual profit by 10.49 %. Moreover, the SHES system demonstrates a lower wind curtailment rate of 0.24 %, in contrast to the 0.37 % observed in individual cases. SHES will play a more significant role in larger wind farm clusters. The aforementioned advantages of SHES systems become even more pronounced in larger wind farm clusters, where their impact on cost reduction and efficiency is significantly amplified.

Dynamic modelling and simulation of the Graz Cycle for a renewable energy system

Critical atmospheric carbon dioxide levels and, in response, an increasing shift toward variable renewable energy sources causes the need of transient operation for thermal power plants coupled with carbon capture and storage. In this regard, dynamic process simulations are a valuable tool for predicting the cycling performance of power plants. In this study, the proposed Graz Cycle, an oxy-combustion power plant fired with natural gas, is investigated and modelled in (a) the steady-state design-point (i.e., full-load), (b) steady-state off-design (i.e., part-load), and (c) dynamic conditions. At full loads, the Graz Cycle power plant achieves a maximum net plant efficiency of 54.5%, comparable to the performance results of the competing Allam cycle found in relevant studies. In the off-design simulation, considering for the first time compressor component maps with variable inlet guide vanes, the load is reduced down to 50% achieving improved performance results compared to previous studies. The dynamic simulation boils down to a ramp rate analysis and shows that practical ramp rates of 5.55 %/min for load decrease and 18.18 %/min for load increase can be reached. The results show that the high ramp rates of the Graz Cycle enable balancing services to the electrical grid next to clean and dispatchable power generation. In this study, a systematic first-principle modelling approach has been developed with the final aim to evolve dynamic models for oxy-combustion carbon capture cycles, and a first-of-its-kind analysis of the dynamic operation of the Graz Cycle is presented.

Modular Linear Power Flow Model Against Large Fluctuations

The linear power flow model based on an empirical operating point is generally employed in power generation dispatch. However, with the increasing penetration of renewable energy sources (RESs) and frequent topology changes, the expansion of the operating region causes considerable modeling errors in the static linear power flow model. In this paper, the sources of linearization error are thoroughly analyzed, and a modular linear power flow modeling framework is proposed. It is found that the key features that affect the accuracy of linearization are the truncation error and its first derivative. This motivates us to cluster historical scenarios based on selected features. The key approach is to individually select a linear power flow model for power system operating conditions with similar linearization error features. This allows us to derive a refined linear power flow model for each cluster to avoid excessive linearization errors using a uniform linear power flow model. Numerical results for several IEEE and Polish test systems show that the linearization error of the proposed method is reduced up to 52.98% compared to current methods. The results demonstrate that the proposed method can use a small number of linearization segments to deal with topology changes.

ETAP software based power flow analysis of 225/60/11KV substation in Tinghir

Through technological advances, such as computer-based software, it is now possible to study and analyze complex electrical system problems and identify potential solutions. This analysis typically includes Load flow analysis (LFA), or power flow analysis (PFA). LFA is a crucial tool in power system engineering used for modeling and designing electrical power networks. It aims to determine the steady-state operating conditions of a power system to ensure a stable and efficient power balance between generation and load demands. LFA helps design robust power networks, maintains system stability, plans maintenance activities, and assists in the economic dispatch of power generation. This study focuses on modelling the 225/60/11 kV substation in Tinghir, a predominantly rural subdivision of the Drâa-Tafilalet region of Morocco. The model used data obtained from SPIE Maroc, specialists in industrial and tertiary electricity. AC and DC load flow analysis is performed on the Tinghir substation auxiliary services under two scenarios: one where the service equipment is powered by the auxiliary service transformer (AST) as the primary source, and another where the equipment supplied by a generator set due to a failure in the main power supply. ETAP software is selected for the simulation due to its advanced real-time power management capabilities and is widely used in power generation, transmission, and distribution systems. The Newton-Raphson method, known for its fast convergence and accuracy, is used for power flow analysis, making it ideal for complex systems. The results evaluate system performance under both normal and backup conditions, ensuring reliable operation of the auxiliary service installation at Tinghir’s substation.

A Semidefinite Programming Method Weighted Sum Based for Solving a Multi-area Emission (Environmental) Economic Dispatch Problem

This paper presents a semi-definite programming (SDP) optimisation approach for solving multi area, emission/economic dispatch (MAEED) problems in thermal power systems. The multi-objective problem was transformed into matrix form as an SDP relaxation problem and subsequently solved with a MATLAB programming suite. The system inequality and equality constraints were entered into a Self-Dual Minimisation (SeDuMi) parser. Simulations were tested on four (4) IEEE benchmark models to validate the efficiency of the proposed SDP method. However, in the generation of the Pareto-fr ont solutions, ideal minimum points were used in the determination of the maximum spread out of the Pareto solutions by the algorithm. This involves the use of a standard weighted sum method in generating the Pareto-optimal solution between two objective functions. Twenty-one (21) runs were carried out for the parameter value to explore the effect of control weight selection k1. A comparative study demonstrated the effectiveness of tie-line constraint and without tie-line constraint, respectively on the optimi sation model for the power dispatch problems solved by using the proposed SDP approach. The optimisation results for the minimum fuel cost using the SDP approach for the case when a tie-line constraint is included was US$ 2,151.7 /hr with minimum emission of 3.4687 Ton/hr. This is lower than US$ 2,162.2 /hr with minimum emission of 3.6662 Ton/hr obtained when the tie-line constraint is not included in the optimisation model on IEEE four-area, sixteen network. Additionally, the comparative results with all other methods reported in the literature for different test cases were also presented in this paper. Lower operational costs were obtained for both tie-lines and no tie-lines power transfer cases, showing that the SDP method performed well in accuracy compared to other methods. The SDP method was employed in this research to deal with the multi-area emission economic power dispatch problem due to its inherent advantages in achieving a global optimal solution and, this approach would also assist the power administrator to come up with an improved decision making in attaining an optimum power dispatch of power generation.

Real-time load dispatch in hydropower plant based on D3QN-PER

The conventional in-plant load dispatch approach has limited decision-making capacity and basic objectives. Small and medium-sized hydropower plants are challenged with the issues of meeting the water dispatch requirements and ensuring the decision-making accuracy and efficiency at the same time as the intensity of peak and frequency modulation of new power systems increases. To match the unit operation strategy and the water dispatch requirements of the power plant, an in-plant load dispatch model that takes into account the needs of both power generation dispatch and water dispatch was proposed in this paper. This was done to address the challenge of meeting the water dispatch requirements. A reinforcement learning approach was used to increase the computation efficiency of optimal load dispatch to address the challenge of ensuring decision-making accuracy and efficiency for real-time load dispatch schemes. According to the application findings at the Shenxigou Hydropower Plant (SXG), (1) this model was built to complete the work of peak and frequency modulation of the power grid while also taking into account the water dispatch requirements of small and medium-sized hydropower plants. With the minimum water usage as the water dispatch objective, this model was able to reduce the calculated water usage for power generation by 1.74 × 106m3 in comparison to the load dispatch table method. The calculated cumulative water level deviation of the model was reduced by 65.14 m when the water dispatch target was water level control. (2) The correctness and effectiveness of the decision-making process could be taken into account by this model. This model could meet the requirements for real-time dispatch decision-making after enhancing load discrete accuracy and adding roll-up computation of forecast information, and its calculation times were 9.46% and 15.10% of those of the load dispatch table technique, respectively.

Development and testing of a 100 kW fuel-flexible micro gas turbine running on 100% hydrogen

Hydrogen, as a carbon-free energy carrier, has emerged as a crucial component in the decarbonization of the energy system, serving as both an energy storage option and fuel for dispatchable power generation to mitigate the intermittent nature of renewable energy sources. However, the unique physical and combustion characteristics of hydrogen, which differ from conventional gaseous fuels such as biogas and natural gas, present new challenges that must be addressed.To fully integrate hydrogen as an energy carrier in the energy system, the development of low-emission and highly reliable technologies capable of handling hydrogen combustion is imperative. This study presents a ground-breaking achievement - the first successful test of a micro gas turbine running on 100% hydrogen with NOx emissions below the standard limits. Furthermore, the combustor of the micro gas turbine demonstrates exceptional fuel flexibility, allowing for the use of various blends of hydrogen, biogas, and natural gas, covering a wide range of heating values. In addition to a comprehensive presentation of the test rig and its instrumentation, this paper illuminates the challenges of hydrogen combustion and offers real-world operational data from engine operation with 100% hydrogen and its blends with methane.

Cost of small-scale dispatchable CO2 capture: Techno-economic comparison and case study evaluation

Dispatchable power sources are crucial for electricity system stability and security of supply. Currently, the United Kingdom uses small-scale (<50 MWe) gas turbines to provide this function. To ensure we reach Net-Zero emissions by 2050, these small-scale dispatchable generators will require CO2 abatement; however, limited studies focus on these forms of power generation.In this work, we use process models developed in our previous studies, to calculate the levelised cost of electricity (LCOE) for a small-scale gas turbine equipped with Carbon Capture and Storage (CCS). The results show that the inclusion of CCS for these dispatchable generators increases the LCOE from 172 £/MWh to 514 £/MWh, almost tripling the cost of electricity. This is due to economies of scale and the low capacity factor. As these generators operate for less than 20% of the year, the levelised cost is drastically higher than other forms of low-carbon power. Dispatchable power is usually more expensive due to the small plant size and transient operation, and including CCS exacerbates this issue. Future work should focus on alternative forms of CO2 capture (designed specifically for small-scale gas turbines) and different dispatchable power generation, i.e., hydrogen and energy storage.

Research of the utilization efficiency of non-flood season flood resources by artificial bee colony algorithm based on multi-strategy hybrid search

Abstract This study aims to investigate the integrated optimization of long-term non-flood season flood dispatching and power generation dispatching and improve the utilization efficiency of non-flood season flood resources. In this study, an artificial bee colony (ABC) algorithm based on a multi-strategy mixed search is supplemented with variable time period characteristics to construct an optimal reservoir operation model with a maximum generating capacity under multiple constraints in a variable time period. The multi-year non-flood season flood operation of the Wanan Reservoir is considered as an example. After optimization, the average annual power generation in the non-flood season was increased by 14.23 million kW·h. The average utilization rate of water resources was increased by 3% over the studied period, and the utilization of flood resources was improved. The ABC optimization algorithm based on a multi-strategy hybrid search could alleviate the contradiction between the selection of the time interval step and the calculation accuracy and convergence speed, and it effectively improved the optimization efficiency and development ability of the standard ABC optimization algorithm. These results can provide suggestions for the improvement of the comprehensive benefits of reservoirs with non-flood floods as an important water resource.

A comparative study of sensible energy storage and hydrogen energy storage apropos to a concentrated solar thermal power plant

To achieve dispatchable and reliable power generation through renewable sources, energy storage is often indispensable. This paper attempts a quantitative investigation and comparison between two different energy storage technologies, Thermal Energy Storage System (TESS), which is already mature, and Hydrogen Energy Storage System (HESS), applied to a common concentrated solar thermal power (CSP) plant. A solar field (SF) comprising parabolic troughs and molten salt as a heat transfer fluid is conceived with a power block running on the Rankine cycle. An integrated TESS system is first considered, and the SF size is iteratively determined, so that plant with TESS has a capacity factor of 100 % for a typical summer day, thereby standardizing the TESS performance. The TESS is then replaced with the HESS, keeping the same SF capacity. The plant capacity and storage performances are then compared with simplistic assumptions. The results show superior TESS performance, while HESS delivers only 58 % capacity factor. TESS also delivers superior energy and power density (68 kWh/m3 and 4.5 kW/m3 for TESS, as opposed to 26 kWh/m3 and 1.8 kWh/m3 for HESS, respectively). However, the specific energy and power for both are comparable (approx. 93 Wh/kg and 6 W/kg, respectively). Although electrolyzer and fuel cell efficiencies individually outperform Rankine efficiency, HESS lags in performance with CSP configuration due to multiple steps of energy conversion.