scholarly journals Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4920
Author(s):  
Florian Schäfer ◽  
Martin Braun
Keyword(s):  
Time Series ◽  
Power System ◽  
High Voltage ◽  
Renewable Energy Sources ◽  
Active Power ◽  
Case Method ◽  
Power System Planning ◽  
System Planning ◽  
Worst Case ◽  
Planning Methods

Integrating active power curtailment (APC) of renewable energy sources (RES) in power system planning reduces necessary investments in the power system infrastructure. In current target grid planning methods, APC is considered by fixed curtailment factors without considering the provided flexibility to its full extent. Time-series-based planning methods allow the integration of the time dependency of RES and loads in power system planning, leading to substantial cost savings compared to the worst-case method. In this paper, we present a multi-year planning strategy for high-voltage power system planning, considering APC as an alternative investment option to conventional planning measures. A decomposed approach is chosen to consider APC and conventional measures in a long-term planning horizon of several years. The optimal investment path is obtained with the discounted cash flow method. A case study is conducted for the SimBench high-voltage urban benchmark system. Results show that the time-series-based method allows for reducing investments by up to 84% in comparison to the worst-case method. Furthermore, a sensitivity analysis shows the variation in total expenditures with changing cost assumptions.

scholarly journals A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

2020 ◽  
Vol 12 (8) ◽  
pp. 3103 ◽  
Author(s):  
Hyojoo Son ◽  
Changwan Kim
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Power System ◽  
Electricity Demand ◽  
Efficient Production ◽  
Power System Planning ◽  
System Planning ◽  
Residential Sector ◽  
Use Of Resources ◽  
Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

scholarly journals Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2091 ◽  
Author(s):  
Ulf Philipp Müller ◽  
Birgit Schachler ◽  
Malte Scharf ◽  
Wolf-Dieter Bunke ◽  
Stephan Günther ◽  
...  
Keyword(s):  
Power System ◽  
High Voltage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Low Voltage ◽  
Distribution Grid ◽  
Power System Planning ◽  
System Planning ◽  
Transmission And Distribution ◽  
And Storage

The energy transition towards renewable and more distributed power production triggers the need for grid and storage expansion on all voltage levels. Today’s power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. Operation and investment costs are minimized by applying a multi-period linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach, storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series led to substantial overall savings.

scholarly journals Integrated techno-economic power system planning of transmission and distribution grids

2019 ◽  
Author(s):  
Ulf Philipp Müller ◽  
Birgit Schachler ◽  
Malte Scharf ◽  
Wolf-Dieter Bunke ◽  
Stephan Günther ◽  
...  
Keyword(s):  
Power System ◽  
High Voltage ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Low Voltage ◽  
Distribution Grid ◽  
Power System Planning ◽  
System Planning ◽  
Transmission And Distribution ◽  
And Storage

The energy transition towards renewable and more decentral power production triggers the need for grid and storage expansion on all voltage levels. Today's power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. System costs are minimized by applying a linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series lead to substantial overall savings.

scholarly journals Active Power Curtailment in Power System Planning

2021 ◽  
pp. 1-1
Author(s):  
Roman Bolgaryn ◽  
Zhenqi Wang ◽  
Alexander Scheidler ◽  
Martin Braun
Keyword(s):  
Power System ◽  
Active Power ◽  
Power System Planning ◽  
System Planning

scholarly journals AI-based Short-Term Electric Time Series Forecasting

2019 ◽  
Vol 8 (10) ◽  
pp. 3255-3261
Keyword(s):  
Time Series ◽  
Power System ◽  
Moving Average ◽  
Time Series Models ◽  
Power System Planning ◽  
System Planning ◽  
Peak Demand ◽  
Auto Regressive ◽  
Proper Power ◽  
Artificial Neural Network Ann

In current scenario of various electrical profiles like load profile, energy met profile, peak demand, etc. are very complex and therefore affected proper power system planning. Electrical forecasting is an important part in proper power system planning. Classical models, i.e., time series models and other conventional models are restricted for linear and stationary electrical profiles. Consequently, these models are not suitable for accurate electrical forecasting. In this paper, artificial neural network (ANN) based forecasting models are proposed to forecast hydro generation, energy met and peak demand. Auto-regressive (AR), moving average (MA), Auto-regressive Moving average (ARMA) and auto-regressive integrated moving average (ARIMA) are also developed to show the effectiveness of ANN based models over time series models. Additionally, best selection of hidden neurons in ANN is also shown here.

scholarly journals Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

2021 ◽  
Author(s):  
Karl‐Kiên Cao ◽  
Jannik Haas ◽  
Evelyn Sperber ◽  
Shima Sasanpour ◽  
Seyedfarzad Sarfarazi ◽  
...  
Keyword(s):  
Power System ◽  
Large Scale ◽  
Energy Systems ◽  
Power System Planning ◽  
System Planning

Simulations of wind power integration with complementary power system planning tools

2008 ◽  
Vol 78 (6) ◽  
pp. 1069-1079 ◽  
Author(s):  
Poul Sørensen ◽  
Ian Norheim ◽  
Peter Meibom ◽  
Kjetil Uhlen
Keyword(s):  
Power System ◽  
Wind Power ◽  
Power System Planning ◽  
System Planning ◽  
Wind Power Integration ◽  
Planning Tools
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