scholarly journals Impact of Different Regulatory Structures on the Management of Energy Communities

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2892 ◽  
Author(s):  
Jordi de la Hoz ◽  
Àlex Alonso ◽  
Sergio Coronas ◽  
Helena Martín ◽  
José Matas
Keyword(s):  
Distributed Generation ◽  
Real Data ◽  
Algebraic Model ◽  
Mixed Integer ◽  
Distributed Energy ◽  
Mixed Integer Linear Program ◽  
Regulatory Frameworks ◽  
Energy Community ◽  
The Impact ◽  
Generation Activity

The following paper aims to prove the importance of embedding the regulatory framework when analyzing the distributed generation activity of an energy community. At present, most of the scientific literature has focused on distributed energy, and energy communities address the issue of regulatory frameworks qualitatively. In this paper, the most representative regulatory frameworks devoted to the promotion of energy communities were analyzed and synthesized, namely, feed-in tariffs, net metering, and the self-consumption scheme. As a result, an algebraic model able to represent the essence of the regulatory structures related to those remuneration mechanisms was obtained. Next, this model was embedded into a physical model, based on real data, previously created. The resulting Mixed Integer Linear Program (MILP) was used to identify the implications of these frameworks. The results demonstrate the impact of regulatory schemes on energy management and economic results of an energy community. Indeed, profitability changes drastically depending on which remuneration scheme is applied to an energy community.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

Energy Mix Forecasting in Abu Dhabi Using Mixed Integer Linear Program

2018 ◽  
Author(s):  
Moza Salim Al Naimi ◽  
Mohamed I. Hassan Ali ◽  
Gento Mogi
Keyword(s):  
Renewable Energy ◽  
Energy Systems ◽  
Energy Transition ◽  
Real Data ◽  
Linear Program ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Further Development

Sustainable energy transition requires a critical prediction for the long-term evolvement of energy systems around the world. It is affected by several factors: the changes in the oil economy, the climate change, and the development of renewable energy supply technologies. The aim of this research is to compare and analyze Abu Dhabi’s generation sector in its transition from complete conventional gas to a mix of conventional and Photovoltaic (PV) energy systems. Employing a Mixed Integer Linear Program (MILP) and PLEXOS software, two capacity expansion scenarios of Abu Dhabi’s generation sector for five years are optimized, analyzed, and compared using a real data from the generation and demand sides. This research means to highlight, to the UAE government, the effect of introducing more renewable energy and to evaluate the performance of the generation side in meeting the forecasted demand. Furthermore, this work opens the doors wide for further development and optimization in the GCC area.

scholarly journals The capacitated plant location problem with customer and supplier matching and interval demands uncertainty

2014 ◽  
Vol 5 ◽  
pp. A03 ◽  
Author(s):  
Kaveh Mehdi ◽  
Maziar Salahi ◽  
Ali Jamalian
Keyword(s):  
Location Problem ◽  
Product Distribution ◽  
Linear Program ◽  
Interval Uncertainty ◽  
Plant Location ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Cost Distribution ◽  
The Impact

The capacitated plant location problem with customer and supplier matching can be modeled as a mixed integer linear program, where the product distribution from plants to customers and the material supply from suppliers to plants are considered together. In order to save allocation cost, distribution trip and a supply trip is merged into one triangular trip. Moreover, vehicles from plants visit a customer and a supplier for each trip. In this paper, we assume interval uncertainties in the demands of costumers. We show that the robust counterpart of the original model with interval uncertainty is equivalent to a larger mixed integer linear program. Finally, the original and robust models are compared on several randomly generated examples showing the impact of uncertainty.

Optimal Design of a Distributed Energy Resources System That Minimizes Cost While Reducing Carbon Emissions

2017 ◽  
Author(s):  
Robert Flores ◽  
Jack Brouwer
Keyword(s):  
Carbon Emissions ◽  
Energy Demand ◽  
Energy Resource ◽  
Minimum Cost ◽  
Mixed Integer ◽  
Distributed Energy ◽  
Gas Generator ◽  
Mixed Integer Linear Program ◽  
The Cost ◽  
The University

From a practical perspective, economics drive the development of distributed energy resource (DER) systems. However, the adoption of a DER system provides an opportunity for the end user to completely control their environmental footprint. This work examines the process of designing a DER system while controlling carbon emissions. A mixed integer linear program (MILP) for sizing and dispatching a DER system is developed. The MILP includes a novel formulation of constraints that govern utility natural gas, generator operational state, and charging of thermal energy storage. The MILP is executed using real energy demand data for the University of California, Irvine to optimally design a DER system that minimizes cost while also reducing carbon emissions by a specified quantity. Two primary technology scenarios are explored (DER including storage with and without electrical export). A trajectory of DER technology adoption is determined for both technology scenarios. The different operational methods through which each system achieved lower carbon emissions at minimum cost is examined. Finally, the cost to reduce carbon emissions is calculated for both technology scenarios.

scholarly journals Individual vs. Community: Economic Assessment of Energy Management Systems under Different Regulatory Frameworks

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 676
Author(s):  
Àlex Alonso ◽  
Jordi de la Hoz ◽  
Helena Martín ◽  
Sergio Coronas ◽  
José Matas
Keyword(s):  
Energy Management ◽  
Economic Assessment ◽  
Individual Case ◽  
Point Of View ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Energy Management Systems ◽  
Regulatory Frameworks ◽  
The Individual ◽  
Feed In Tariff

In the context of the increasing popularity of self-sufficient communities around the globe, this study aims to compare the economic performance of energy management in two distinct situations: whether it is conducted individually or collectively within a community. After setting the context and completing a literature review, a research gap concerning the influence of regulatory frameworks in the economic results is identified. Therefore, this work presents this comparison under several frameworks employed to promote renewable energy, in order to provide a more realistic point of view and deliver insights in policy making. To this end, a mixed integer linear program (MILP) is developed, and the formulation of three key regulatory schemes is embedded into it: feed-in tariff, net metering, and self-consumption schemes. A what-if analysis is performed in order to take into account different combinations of rewarding parameters for each regulatory framework, as well as different profiles of consumption for the individual case. Results show that energy management within a community improves the overall average benefit of the customers up to 0.44 €/day·dwelling, for all of the studied frameworks except feed-in-tariff and some instances of type-B self-consumption, which can reduce it up to −0.87 €/day·dwelling. The conclusions determine fundamental differences between regulatory schemes and their suitability to promote collective or individual facilities, and emphasize the need to design a set of policies that take into account the habits of consumption of the individuals to foster effectively energy communities.

scholarly journals A Demand Response Approach to Scheduling Constrained Load Shifting

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1752 ◽  
Author(s):  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Distributed Generation ◽  
Demand Response ◽  
Energy Resources ◽  
Distributed Energy Resources ◽  
Virtual Power ◽  
Distributed Energy ◽  
Load Shifting ◽  
Operation Costs ◽  
The Impact

Demand response (DR) and its advantages are nowadays unquestionable due to the success of several recent implementations of DR programs. Improved methodologies and approaches are needed for the adequate consumers’ schedule in DR events, taking the consumers’ behaviour and preferences into account. In this paper, a virtual power player manages DR programs, minimizing operation costs, respecting the consumption shifting constraints. The impact of the consumption shifting in the target periods is taken into consideration. The advantages of the DR use in comparison with distributed generation (DG) are evaluated. Two scenarios based on 218 consumers in a frame of 96 periods have been implemented. It is demonstrated the advantages of DR in the operation of distributed energy resources, namely when considering the lack of supply.

scholarly journals Characterizing and Visualizing the Impact of Energy Storage on Renewable Energy Curtailment in Bulk Power Systems

2021 ◽  
Vol 11 (3) ◽  
pp. 1135
Author(s):  
Zhongjie Guo ◽  
Wei Wei ◽  
Maochun Wang ◽  
Jian Li ◽  
Shaowei Huang ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Power Systems ◽  
Empirical Distribution ◽  
Evaluation Model ◽  
Mixed Integer ◽  
Storage Unit ◽  
Mixed Integer Linear Program ◽  
Energy Storage Unit ◽  
The Impact

The uncertain natures of renewable energy lead to its underutilization; energy storage unit (ESU) is expected to be one of the most promising solutions to this issue. This paper evaluates the impact of ESUs on renewable energy curtailment. For any fixed renewable power output, the evaluation model minimizes the total amount of curtailment and is formulated as a mixed integer linear program (MILP) with the complementarity constraints on the charging and discharging behaviors of ESUs; by treating the power and energy capacities of ESUs as parameters, the MILP is transformed into a multi-parametric MILP (mp-MILP), whose optimal value function (OVF) explicitly maps the parameters to the renewable energy curtailment. Further, given the inexactness of uncertainty’s probability distribution, a distributionally robust mp-MILP (DR-mp-MILP) is proposed that considers the worst distribution in a neighborhood of the empirical distribution built by the representative scenarios. The DR-mp-MILP has a max–min form and is reformed as a canonical mp-MILP by duality theory. The proposed method was validated on the modified IEEE nine-bus systems; the parameterized OVFs provide insightful suggestions on storage sizing.

scholarly journals Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3750
Author(s):  
Homam Nikpey Somehsaraei ◽  
Susmita Ghosh ◽  
Sayantan Maity ◽  
Payel Pramanik ◽  
Sudipta De ◽  
...  
Keyword(s):  
Machine Learning ◽  
Distributed Generation ◽  
Renewable Resources ◽  
Spatial Clustering ◽  
Real Data ◽  
Data Filtering ◽  
Distributed Energy ◽  
Micro Gas Turbine ◽  
Monitoring Applications ◽  
Normal Performance

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.

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