Dynamic Rolling Horizon-Based Robust Energy Management for Microgrids Under Uncertainty

Within the last few years, the trend towards more distributed, renewable energy sources has led to major changes and challenges in the electricity sector. To ensure a stable electricity distribution in this changing environment, we propose a robust energy management approach to deal with uncertainty occurring in microgrids. For this, we combine robust optimization with a rolling horizon framework to obtain an algorithm that is both, tractable and can deal with the considered uncertainty. The main contribution of this work lies within the development and testing of a dynamic scheduling tool, which identifies good starting time slots for the rolling horizon. Combining this scheduling tool with the rolling horizon framework results in a dynamic rolling horizon model, which better integrates uncertainty forecasts and realizations of uncertain parameters into the decision-making process. A case study reveals that the dynamic rolling horizon model outperforms the classical version by up to 57% in costs and increases the local use of PV by up to 11%.Comment: 29 pages, 6 figure

Threshold-Based Algorithms for an Online Rolling Horizon Framework Under Uncertainty -- With an Application to Energy Management

Decision problems encountered in practice often possess a highly dynamic and uncertain nature. In particular fast changing forecasts for parameters (e.g., photovoltaic generation forecasts in the context of energy management) pose large challenges for the classical rolling horizon framework. Within this work, we propose an online scheduling algorithm for a rolling horizon framework, which directly uses short-term forecasts and observations of the uncertainty. The online scheduling algorithm is based on insights and results from combinatorial online optimization problems and makes use of key properties of robust optimization. Applied within a robust energy management approach, we show that the online scheduling algorithm is able to reduce the total electricity costs within a local microgrid by more than 85% compared to a classical rolling horizon framework and by more than 50% compared to a tailor-made dynamic, yet still offline rolling horizon framework. A detailed analysis provides insights into the working of the online scheduling algorithm under different underlying forecast error distributions.Comment: 40 pages, 14 figure

Grid-Aware Real-Time Control and Balancing Between Microgrids

Due to the energy transition, lots of research has been conducted within the last decade on the topics of energy management systems or local energy trading approaches, often on the day-ahead or intraday level. A large majority of these approaches focuses on 15 or 60-minute time intervals for their operation, however, the question of how the planned solutions are realized within these time intervals is often left unanswered. Within this work, we aim to close this gap and propose a real-time balancing and control approach for a set of microgrids, which implements the day-ahead solutions. The approach is based on a three-step framework, in which the first step consists of ensuring the feasibility of devices within the microgrids. The second step focuses on the grid constraints of the connecting medium voltage grid using the DC power flow formulation due to the running time requirements of a real-time approach. The last step is to propagate the solution into the individual microgrids, where the allocated power needs to be distributed among the devices and households. Within a case study, we show that the proposed real-time control approach works as intended and is comparable to an optimal offline algorithm under some mild assumptions.Comment: 35 pages, 6 figures, 2 table

A Classification Scheme for Local Energy Trading

The current trend towards more renewable and sustainable energy generation leads to an increased interest in new energy management systems and the concept of a smart grid. One important aspect of this is local energy trading, which is an extension of existing electricity markets by including prosumers, who are consumers also producing electricity. Prosumers having a surplus of energy may directly trade this surplus with other prosumers, which are currently in demand. In this paper, we present an overview of the literature in the area of local energy trading. In order to provide structure to the broad range of publications, we identify key characteristics, define the various settings, and cluster the considered literature along these characteristics. We identify three main research lines, each with a distinct setting and research question. We analyze and compare the settings, the used techniques, and the results and findings within each cluster and derive connections between the clusters. In addition, we identify important aspects, which up to now have to a large extent been neglected in the considered literature and highlight interesting research directions, and open problems for future work.Comment: 38 pages, 1 figure, This work has been submitted and accepted at OR Spectru

Robust Energy Management for a Microgrid

Due to the increasing penetration of photovoltaic (PV) systems, electric vehicles (EV) and other smart devices on a household level, the role of consumers changes from pure consumption to production and storage of electricity. These prosumers will also directly participate in future electricity markets. To compensate for the small scale and the fluctuations in their demand and production, one promising approach for prosumers is to form small energy communities or microgrids, and participate in the electricity markets as one entity. A challenge for these microgrids is to find an optimal energy management strategy, mainly due to the uncertainty in electricity prices, in PV generation as Well as in the prosumer loads. To integrate this uncertainty into the planning, an adaptive robust optimization approach using linear decision rules is proposed in this paper. The linear decision rules allow for a delayed determination of some of the decisions and can therefore adapt to realizations of the uncertainty. Three different uncertainty scenarios are used to evaluate and compare the proposed approach in a case study and to get more structural insights into the efficiency of the approach

Modeling and demonstrating the effect of human decisions on the distribution grid

Demand-side management methods such as flexibility and local electricity market studies often do not include varying human decisions, guided by behavior and preference, and the effect these have on the markets and, consequently, on the load of the distribution grid. Studies have shown that, given the right driving motive, human decisions can have an effect and are essential to consider. However, human decisions and their motives are complex and challenging to model, and the effects are not entirely known. Therefore, there is a need to break down human decisions and their motives into modeling parameters to see how these affect the distribution grid. This study aims to model human decisions and see the effect of varying the motives behind these decisions on the operation of distribution grids. First, social factors are explored to determine relevant human decisions. Second, by determining what and how motives drive these decisions. Finally, varying motives and changing human decisions are implemented and simulated in fixed extreme cases. It was found that human decisions can have significant positive and negative effects on the operation of distribution grids depending on the motive and that these motives should be treated delicately