Revisiting the Merit-Order Effect of Renewable Energy Sources

An on-going debate in the energy economics and power market community has raised the question if energy-only power markets are increasingly failing due to growing feed-in shares from subsidized renewable energy sources (RES). The short answer to this is: No, they are not failing. Energy-based power markets are, however, facing several market distortions, namely from the gap between the electricity volume traded at day-ahead markets versus the overall electricity consumption as well as the (wrong) regulatory assumption that variable RES generation, i.e., wind and photovoltaic (PV), truly have zero marginal operation costs. In this paper we show that both effects over-amplify the well-known merit-order effect of RES power feed-in beyond a level that is explainable by underlying physical realities, i.e., thermal power plants being willing to accept negative electricity prices to be able to stay online due to considerations of wear & tear and start-stop constraints. We analyze the impacts of wind and PV power feed-in on the day-ahead market for a region that is already today experiencing significant feed-in tariff (FIT)-subsidized RES power feed-in, the EPEX German-Austrian market zone ($\approx\,$20% FIT share). Our analysis shows that, if the necessary regulatory adaptations are taken, i.e., increasing the day-ahead market's share of overall load demand and using the true marginal costs of RES units in the merit-order, energy-based power markets can remain functional despite high RES power feed-in.Comment: Working Paper (9 pages, 11 figures, 5 tables) - Some revisions since last version (10 February 2014). (Under 2nd review for IEEE Transactions on Power Systems

Large Scale Deployment of Renewables for Electricity Generation

Comparisons of resource assessments suggest resource constraints are not an obstacle to the large-scale deployment of renewable energy technologies. Economic analysis identifies barriers to the adoption of renewable energy sources resulting from market structure, competition in an uneven playing field and various non-market place barriers. However, even if these barriers are removed, the problem of ‘technology lock-out’ remains. The key policy response is strategic deployment coupled with increased R&D support to accelerate the pace of improvement through market experience. The paper suggests significant contributions from various technologies, but does not assess their optimal or maximal market share

Optimization approaches for exploiting the load flexibility of electric heating devices in smart grids

Energy systems all over the world are undergoing a fundamental transition to tackle climate change and other environmental challenges. The share of electricity generated by renewable energy sources has been steadily increasing. In order to cope with the intermittent nature of renewable energy sources, like photovoltaic systems and wind turbines, the electrical demand has to be adjusted to their power generation. To this end, flexible electrical loads are necessary. Moreover, optimization approaches and advanced information and communication technology can help to transform the traditional electricity grid into a smart grid. To shift the electricity consumption in time, electric heating devices, such as heat pumps or electric water heaters, provide significant flexibility. In order to exploit this flexibility, optimization approaches for controlling flexible devices are essential. Most studies in the literature use centralized optimization or uncoordinated decentralized optimization. Centralized optimization has crucial drawbacks regarding computational complexity, privacy, and robustness, but uncoordinated decentralized optimization leads to suboptimal results. In this thesis, coordinated decentralized and hybrid optimization approaches with low computational requirements are developed for exploiting the flexibility of electric heating devices. An essential feature of all developed methods is that they preserve the privacy of the residents. This cumulative thesis comprises four papers that introduce different types of optimization approaches. In Paper A, rule-based heuristic control algorithms for modulating electric heating devices are developed that minimize the heating costs of a residential area. Moreover, control algorithms for minimizing surplus energy that otherwise could be curtailed are introduced. They increase the self-consumption rate of locally generated electricity from photovoltaics. The heuristic control algorithms use a privacy-preserving control and communication architecture that combines centralized and decentralized control approaches. Compared to a conventional control strategy, the results of simulations show cost reductions of between 4.1% and 13.3% and reductions of between 38.3% and 52.6% regarding the surplus energy. Paper B introduces two novel coordinating decentralized optimization approaches for scheduling-based optimization. A comparison with different decentralized optimization approaches from the literature shows that the developed methods, on average, lead to 10% less surplus energy. Further, an optimization procedure is defined that generates a diverse solution pool for the problem of maximizing the self-consumption rate of locally generated renewable energy. This solution pool is needed for the coordination mechanisms of several decentralized optimization approaches. Combining the decentralized optimization approaches with the defined procedure to generate diverse solution pools, on average, leads to 100 kWh (16.5%) less surplus energy per day for a simulated residential area with 90 buildings. In Paper C, another decentralized optimization approach that aims to minimize surplus energy and reduce the peak load in a local grid is developed. Moreover, two methods that distribute a central wind power profile to the different buildings of a residential area are introduced. Compared to the approaches from the literature, the novel decentralized optimization approach leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Paper D introduces uncertainty handling control algorithms for modulating electricheating devices. The algorithms can help centralized and decentralized scheduling-based optimization approaches to react to erroneous predictions of demand and generation. The analysis shows that the developed methods avoid violations of the residents\u27 comfort limits and increase the self-consumption rate of electricity generated by photovoltaic systems. All introduced optimization approaches yield a good trade-off between runtime and the quality of the results. Further, they respect the privacy of residents, lead to better utilization of renewable energy, and stabilize the grid. Hence, the developed optimization approaches can help future energy systems to cope with the high share of intermittent renewable energy sources

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Large Scale Deployment of Renewables for Electricity Generation

Comparisons of resource assessments suggest resource constraints are not an obstacle to the large-scale deployment of renewable energy technologies. Economic analysis identifies barriers to the adoption of renewable energy sources resulting from market structure, competition in an uneven playing field and various non-market place barriers. However, even if these barriers are removed, the problem of ‘technology lock-out’ remains. The key policy response is strategic deployment coupled with increased R&D support to accelerate the pace of improvement through market experience. The paper suggests significant contributions from various technologies, but does not assess their optimal or maximal market share.technology policy, renewable energy, learning externalities, market structure

Interactive Demand Shifting in the context of Domestic Micro-Generation

The combination of ubiquitous computing and emerging energy technologies is radically changing the home energy landscape. Domestic micro-generation, dominated by solar photovoltaic, is increasing at a rapid pace. This represents an opportunity for creating and altering energy behaviours. However, these transformations generate new challenges that we call the domestic energy gap: domestic electricity consumption and microgeneration are out of sync. Micro-generation is mainly uncontrollable production relying on weather while domestic energy consumption tends to happen mostly during the evening. This thesis focuses on understanding and supporting new domestic practices in the context of domestic solar electricity generation, looking at ‘Demand-Shifting’. Specifically, we look at how can digital tools leverage Demand-Shifting practices in the context of domestic micro-generation? Relying on a mixed-method approach, we provide a qualitative and quantitative answer with the collaboration of 38 participating households in several field studies including two spanning more than eight months. Through a deep investigation of laundry and electric mobility routines in the context of domestic micro-generation, we emphasised a natural engagement into Demand-Shifting which appeared as a complex and time-consuming task for participants which was not visible when we analysed their quantitative data. We revealed this complexity through Participatory Data Analyses, a method we designed to analyse the data in collaboration with the participating householders. This provided us with a comprehensive view of the relationship between domestic micro-generation and daily routines. Finally, we highlight the need for timely and contextual support through the deployment of interventions in-the-wild. Building on discussions of our findings in perspective of the literature, we propose a conceptual framework to support domestic interactive Demand-Shifting

A model predictive control-based energy management scheme for hybrid storage system in islanded microgrids

Model predictive control (MPC) facilitates online optimal resource scheduling in electrical networks, thermal systems, water networks, process industry to name a few. In electrical systems, the capability of MPC can be used not only to minimise operating costs but also to improve renewable energy utilisation and energy storage system degradation. This work assesses the application of MPC for energy management in an islanded microgrid with PV generation and hybrid storage system composed of battery, supercapacitor and regenerative fuel cell. The objective is to improve the utilisation of renewable generation, the operational efficiency of the microgrid and the reduction in rate of degradation of storage systems. The improvements in energy scheduling, achieved with MPC, are highlighted through comparison with a heuristic based method, like Fuzzy inference. Simulated behaviour of an islanded microgrid with the MPC and fuzzy based energy management schemes will be studied for the same. Apart from this, the study also carries out an analysis of the computational demand resulting from the use of MPC in the energy management stage. It is concluded that, compared to heuristic methods, MPC ensures improved performance in an islanded microgrid.This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska Curie under Grant 675318 (INCITE), in part by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI under Grant MDM-2016-0656, and in part by the Spanish National Project DOVELAR (MCIU/AEI/FEDER, UE) under Grant RTI2018-096001-B-C32.Peer ReviewedPostprint (published version