scholarly journals Short-Term Scheduling of Expected Output-Sensitive Cascaded Hydro Systems Considering the Provision of Reserve Services

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2477
Author(s):  
Ruhong Zhong ◽  
Chuntian Cheng ◽  
Shengli Liao ◽  
Zhipeng Zhao
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Short Term ◽  
Iterative Approximation ◽  
Peak Shaving ◽  
Renewable Power ◽  
Optimality Algorithm ◽  
Output Characteristics ◽  
Iterative Approximation Method

The integration of large-scale wind and solar power into the power grid brings new challenges to the security and stability of power systems because of the uncertainty and intermittence of wind and solar power. This sensitive expected output has real implications for short-term hydro scheduling (STHS), which provides reserve services to alleviate electrical perturbations from wind and solar power. This paper places an emphasis on the sensitive expected output characteristics of hydro plants and their effect on reserve services. The multi-step progressive optimality algorithm (MSPOA) is used for the STHS problem. An iterative approximation method is proposed to determine the forebay level and output under the condition of the sensitive expected output. Cascaded hydro plants in the Wujiang River are selected as an example. The results illustrate that the method can achieve good performance for peak shaving and reserve services. The proposed approach is both accurate and computationally acceptable so that the obtained hydropower schedules are in accordance with practical circumstances and the reserve can cope with renewable power fluctuations effectively.

Hybrid hydropower-connected floating solar PV plants: impact of the downstream water release constraint

2021 ◽  
Author(s):  
Stanislas Merlet ◽  
Magnus Korpås ◽  
Bjørn Thorud
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Large Scale ◽  
Solar Power ◽  
Hydropower Plant ◽  
Solar Pv ◽  
Reservoir Operations ◽  
Water Release ◽  
Renewable Power ◽  
Scale Integration

<p>Solar and wind power continue to dominate the renewable energy expansion, jointly accounting for more than 90% of the new capacity installed in 2019. Hydropower, however, still accounts for 47% of the 2,537 GW of global renewable power in operation. Solar power continued to lead the yearly expansion, for the fourth year in a row, with an annual increase of +20% while hydropower capacity increased by +1%. However, the inherent intermittency and stochastic nature of solar PV is a well-known obstacle to the further large-scale integration of the technology in existing power systems. Large-scale reservoir hydropower offers a cost-competitive, mature and dispatchable alternative that can provide both production flexibility and storage. Nonetheless, the costs of large hydropower are highly site-specific and new capacity development has been more and more constrained by substantial environmental and social impacts in many places worldwide. Solar power and hydropower resources have been identified to be quite complementary and hybrid plants could have many flexibility benefits in addition to the increase of renewable energy production. In this context, floating solar PV (FPV) on hydropower reservoirs is emerging as a relevant solution to accommodate both energy sources at the same location.</p><p>Adding FPV to an existing hydropower plant, aiming at hybridizing the output, might impact its reservoir operations and water-related constraints need to be carefully considered. Solar PV can contribute to saving water on mid- to long-term scheduling considering that solar energy generation corresponds in some extent to non-turbined water, i.e. saved energy. Besides, on the short-term time scale, one of the main benefits is that hydropower could, in some extent, compensate for the variability of PV generation by its rapidly adjustable output. In practice, a utility-scale solar PV plant could lose several MW of generation in seconds, if a large cloud passes, for example. To avoid consequences on the power grid, this energy loss would need to be translated almost immediately (according to available capacity and ramp rates capabilities) to hydropower generation, meaning substantial (and potentially more frequent) surges in released water downstream.</p><p>The presentation investigates these opportunities and challenges linked to reservoir operations of hybrid hydropower-connected floating solar PV plants and provide inputs on optimal solutions.</p>

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

scholarly journals Day-ahead renewable scenario forecasts based on generative adversarial networks

2020 ◽  
Author(s):  
Congmei Jiang ◽  
Yongfang Mao ◽  
Yi Chai ◽  
Mingbiao Yu
Keyword(s):  
Power Systems ◽  
Renewable Resources ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Power Sources ◽  
Renewable Power ◽  
Adversarial Networks ◽  
Future Behavior ◽  
Large Scale Integration ◽  
Scale Integration

<p>With the increasing penetration of renewable resources such as wind and solar, the operation and planning of power systems, especially in terms of large-scale integration, are faced with great risks due to the inherent stochasticity of natural resources. Although this uncertainty can be anticipated, the timing, magnitude, and duration of fluctuations cannot be predicted accurately. In addition, the outputs of renewable power sources are correlated in space and time, and this brings further challenges for predicting the characteristics of their future behavior. To address these issues, this paper describes an unsupervised method for renewable scenario forecasts that considers spatiotemporal correlations based on generative adversarial networks (GANs), which have been shown to generate high-quality samples. We first utilized an improved GAN to learn unknown data distributions and model the dynamic processes of renewable resources. We then generated a large number of forecasted scenarios using stochastic constrained optimization. For validation, we used power-generation data from the National Renewable Energy Laboratory wind and solar integration datasets. The experimental results validated the effectiveness of our proposed method and indicated that it has significant potential in renewable scenario analysis.</p>

scholarly journals Optimization and Visualization Tools for Situational Awareness in Highly Renewable Power Systems

2020 ◽  
Author(s):  
Paul Cuffe
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Situational Awareness ◽  
Short Term ◽  
Renewable Power ◽  
Output Fluctuations ◽  
System Operator ◽  
Visualization Tools ◽  
Near Term

<div>As submitted to IEEE EnergyCon 2020<br></div><div><br></div><div><br></div><div>Abstract:<br></div><div><br></div><div>This paper proposes new tools for predicting and visualising the plausible near term shifts in branch loading that may arise due to output fluctuations from renewable generators. These tools are proposed to enhance situational awareness for control room operators, by providing early warnings of where bottlenecks may manifest in a transmission system. For predicting plausible branch loading shifts, a linear optimal power flow formulation is presented which uses a novel objective function to characterise the maximum loading a branch could be exposed to in the short term. This analysis therefore identifies which branches could become overloaded due to shifts in output from volatile generators. Equivalently, these branches can be seen as congestion bottlenecks which may cause curtailment of renewable generation. To allow the system operator to maintain awareness of such potentialities, these congestable branches are highlighted on a system diagram which is drawn to explicitly portray the electrical distance between components in the network.</div><br>

scholarly journals Development of an Optimization Model to Manage Imbalances in Power Systems by Local Power Retail Companies

2019 ◽  
Vol 260 ◽  
pp. 02003
Author(s):  
Ryuji Matsuhashi
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Mixed Integer ◽  
Stable Operation ◽  
Forecast Errors ◽  
Local Power ◽  
Power Sources ◽  
Renewable Power ◽  
Short Lead Time ◽  
High Tariff

The feed-in tariff (FIT) programs resulted in rapid growth of renewable power sources in various countries. In Japan, the program particularly triggered explosive growth of solar power generations because of its short lead-time and high tariff level. Although mass introduction of renewable power sources certainly contributes to reduce CO2 emissions, it causes serious instability issues in power systems. One of the most serious issues is management of imbalances resulting from forecast errors in solar power generations. These imbalances must be compensated so as to keep stable operation in power systems. On the other hand, local power retail companies are increasing nowadays in various countries including Japan. These companies are mainly procuring renewable power sources such as solar power systems.Taking these circumstances into consideration, this article aims at exploring measures to manage the imbalances of power systems by local power retail companies. For this purpose, we developed a model in mixed integer linear programming to operate power systems dealing with the imbalances. Evaluated results using the model indicated the followings; appropriate adoption of stationary or home batteries is shown to economically compensate the imbalances by local power retail companies.

scholarly journals The Role of Local Citizen Energy Communities in the Road to Carbon-Neutral Power Systems: Outcomes from a Case Study in Portugal

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 840-863
Author(s):  
Hugo Algarvio
Keyword(s):  
Power Systems ◽  
Electricity Markets ◽  
Power Plants ◽  
Large Scale ◽  
Solar Power ◽  
Hydroelectric Power ◽  
Solar Power Plants ◽  
Carbon Neutral

Global warming contributes to the worldwide goal of a sustainable carbon-neutral society. Currently, hydroelectric, wind and solar power plants are the most competitive renewable technologies. They are limited to the primary resource availability, but while hydroelectric power plants (HPPs) can have storage capacity but have several geographical limitations, wind and solar power plants have variable renewable energy (VRE) with stochastic profiles, requiring a substantially higher investment when equipped with battery energy storage systems. One of the most affordable solutions to compensate the stochastic behaviour of VRE is the active participation of consumers with demand response capability. Therefore, the role of citizen energy communities (CECs) can be important towards a carbon-neutral society. This work presents the economic and environmental advantages of CECs, by aggregating consumers, prosumers and VRE at the distribution level, considering microgrid trades, but also establishing bilateral agreements with large-scale VRE and HPPs, and participating in electricity markets. Results from the case-study prove the advantages of CECs and self-consumption. Currently, CECs have potential to be carbon-neutral in relation to electricity consumption and reduce consumers’ costs with its variable term until 77%. In the future, electrification may allow CECs to be fully carbon-neutral, if they increase their flexibility portfolio.

scholarly journals Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4349 ◽  
Author(s):  
Tian Shi ◽  
Fei Mei ◽  
Jixiang Lu ◽  
Jinjun Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Phase Space ◽  
Power Systems ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Load Forecasting ◽  
Reconstruction Algorithm ◽  
Phase Space Reconstruction ◽  
Load Prediction ◽  
Short Term ◽  
New Energy

With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

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