The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

Schmitt, Thomas; Rodemann, Tobias; Adamy, Jürgen

doi:10.3390/en14092569

Cited by 9 publications

(3 citation statements)

References 30 publications

(57 reference statements)

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“…The third objective consists of the main factors of battery degradation, i.e., the energy throughput, the charging rate and the average state of charge [43,44],…”

Section: System Descriptionmentioning

confidence: 99%

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

et al. 2022

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We present a new two-step approach for automatized a posteriori decision making in multi-objective optimization problems, i.e., selecting a solution from the Pareto front. In the first step, a knee region is determined based on the normalized Euclidean distance from a hyperplane defined by the furthest Pareto solution and the negative unit vector. The size of the knee region depends on the Pareto front’s shape and a design parameter. In the second step, preferences for all objectives formulated by the decision maker, e.g., 50–20–30 for a 3D problem, are translated into a hyperplane which is then used to choose a final solution from the knee region. This way, the decision maker’s preference can be incorporated, while its influence depends on the Pareto front’s shape and a design parameter, at the same time favorizing knee points if they exist. The proposed approach is applied in simulation for the multi-objective model predictive control (MPC) of the two-dimensional rocket car example and the energy management system of a building.

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“…The third objective consists of the main factors of battery degradation, i.e., the energy throughput, the charging rate and the average state of charge [43,44],…”

Section: System Descriptionmentioning

confidence: 99%

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

et al. 2022

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“…The literature mentioned above selected MPC since the increased complexity of MPC approach pays off in terms of cost minimisation. Although the optimal solution is sensible to forecast errors [36], developments in smart metering technology and machine learning algorithms are improving power and control devices as well as forecasting methods. The selection and incorporation of a forecasting methodology and the design of an UC optimisation model are cornerstones of the MPC.…”

Section: Literature Reviewmentioning

confidence: 99%

A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids

2021

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Renewable-powered “undergrid mini-grids” (UMGs) are instrumental for electrification in developing countries. An UMG can be installed under a—possibly unreliable— main grid to improve the local reliability or the main grid may “arrive” and connect to a previously isolated mini-grid. Minimising costs is key to reducing risks associated with UMG development. This article presents a novel market-logic strategy for the optimal operation of UMGs that can incorporate multiple types of controllable loads, customer smart curtailment based on reliability requirements, storage management, and exports to and imports from a main grid, which is subject to failure. The formulation results in a mixed-integer linear programming model (MILP) and assumes accurate predictions of the following uncertain parameters: grid spot prices, outages of the main grid, solar availability and demand profiles. An AC hybrid solar-battery-diesel UMG configuration from Nigeria is used as a case example, and numerical simulations are presented. The load-following (LF) and cycle-charging (CC) strategies are compared with our predictive strategy and HOMER Pro’s Predictive dispatch. Results prove the generality and adequacy of the market-logic dispatch model and help assess the relevance of outages of the main grid and of spot prices above the other uncertain input factors. Comparison results show that the proposed market-logic operation approach performs better in terms of cost minimisation, higher renewable fraction and lower diesel use with respect to the conventional LF and CC operating strategies.

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“…Out of the two methods one works according to the available dataset whereas other being a hybrid method depends on daily weather forecast. The application of solar energy system and its integration with grid examining the grid stability with unit commitment along with economic dispatch is explained [7]. In addition, the probabilistic forecaster topology for regional applications is also discussed.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid topology for PV Forecasting and its application in Energy Management for Microgrid

Gupta

Singh

2022

Preprint

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Photovoltaic (PV) system is the most promising Renewable technology. PV Forecasting is needed due to the natural variation of meteorological variables such as solar radiations and climatic conditions. Due to this the power produced by a PV system is always non-linear. A hybrid forecasting approach has been presented in this paper. This hybrid approach is a combination of statistical approach, machine learning approach as well as physical approach. The machine learning approach uses a single layer and double layer perception concept based on Artificial Neural Network (ANN) whereas the statistical and physical approach used data driven formulation concept for forecasting. This data are based on historical analysis as well as they are also helpful in future forecasting. An implementation of a two-layer hierarchical model for Energy Management System (EMS) of islanded solar Microgrid (MG) is presented. The MG control employing forecast module and simulation process has been discussed briefly. The whole forecasting has been done on the basis of real-time data (RTD) of an industry and has been simulated in HOMER student’s version software.

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The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

Cited by 9 publications

References 30 publications

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

Incorporating Human Preferences in Decision Making for Dynamic Multi-Objective Optimization in Model Predictive Control

A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids

A Hybrid topology for PV Forecasting and its application in Energy Management for Microgrid

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