Optimal Energy and Flexibility Dispatch of Grid-Connected Microgrids

This thesis proposes an optimization model to efficiently schedule energy and flexibilities of a grid-connected microgrid (MG) with non-dispatchable renewable energy sources and battery energy storages (BESs). The model can also be used to coordinate the MG operation with the connected upstream distribution grid and to assess the MG flexibility considering economic viability, technical feasibility, and BES degradation. The performance of the model was tested for both deterministic and stochastic formulations using two solution approaches i.e., day-ahead and rolling horizon, in different simulation and demonstration test cases. In these test cases, the model optimizes the schedule of the MG resources and the energy exchange with the connected main grid, while satisfying the constraints and operational objectives of the MG. The flexibilities from the MG would also be optimized when the MG provided flexibility services (FSs) to the distribution systems. The coordination with the distribution system operator (DSO) was proposed to ensure that the microgrid operation would not violate the technical constraints of the distribution grid. \ua0Two types of test systems were used for the simulations studies: 1) distribution grids with grid-connected MGs and 2) building MGs (BMGs). The distribution test systems included the 12-kV electrical distribution grid of the Chalmers campus and a 12.6-kV 33-bus standard test system, while the BMGs were based on real residential buildings i.e., the HSB LL building and the Brf Viva buildings. Results of the Chalmers’ test case showed that the MGs’ economic optimization could reduce the annual cost for the DSO by up to 2%. Centralized coordination, where the MG resources were scheduled by the DSO, led to an even higher reduction, although it also led to sub-optimal solutions for the MGs. Decentralized coordination was applied on the 33-bus network with a bilevel optimization framework for energy and flexibility dispatch. Two types of FSs were integrated in the bilevel model i.e., the baseline (FS-B) and the capacity limitation (FS-C). The latter has found to be more promising, as it could offer economic incentives for both the DSO and the MGs. In the studies of the BMGs, the BESs were modeled considering both degradation and real-life operation characteristics derived from measurements conducted at the buildings. Results showed that the annual building energy and BES degradation cost could be reduced by up to 3% compared to when the impact of BES degradation was neglected in the energy scheduling. With the participation of the BMG in FS-C provision, the building’s operation cost could be further reduced depending on the flexibility price. A 24-h simulation of the BMG’s operation yielded an economic value of flexibility of at least 7% of its daily energy and peak power cost, while the DSO could benefit from the FS assuming that the dispatched flexibility could be used to reduce the subscription fee that guarantees a certain power level. For frequent flexibility provision i.e., multiple times within a year, the value of flexibility for the MG operator could be reduced due to the BES degradation.\ua0To demonstrate the practical use of the proposed model, an energy management system was designed to integrate the model and employ it to optimize the energy schedule of the BMGs’ BESs and energy exchange with the main grid. The energy dispatch was performed in real-time based on the model’s decisions in real demonstration cases. The demonstration results showed the benefits of the model in that it helped reduce the energy cost of the BMG both in short term and in long term. The model can also be used by the MG operators to quantify the potential and assess the value of microgrid flexibility. Moreover, with the help of this model, the MG can be employed as a flexible resource and reduce the operation cost of the connected distribution grid

Adequacy of neural networks for wide-scale day-ahead load forecasts on buildings and distribution systems using smart meter data

Power system operation increasingly relies on numerous day-ahead forecasts of local, disaggregated loads such as single buildings, microgrids and small distribution system areas. Various data-driven models can be effective predicting specific time series one-step-ahead. The aim of this work is to investigate the adequacy of neural network methodology for predicting the entire load curve day-ahead and evaluate its performance for a wide-scale application on local loads. To do so, we adopt networks from other short-term load forecasting problems for the multi-step prediction. We evaluate various feed-forward and recurrent neural network architectures drawing statistically relevant conclusions on a large sample of residential buildings. Our results suggest that neural network methodology might be ill-chosen when we predict numerous loads of different characteristics while manual setup is not possible. This article urges to consider other techniques that aim to substitute standardized load profiles using wide-scale smart meters data

Predictive Energy Management of Islanded Microgrids with Photovoltaics and Energy Storage

Islanded microgrids powered primarily by photovoltaic (PV) arrays present a challenging control problem due to the intermittent production and the relatively close scale between the sources and the loads. Energy storage in such microgrids plays an important role in balancing supply with demand, and in extending operation during periods when the PV supply is not available or insufficient. The efficient operation of such microgrids requires effective management of all resources. A predictive energy management strategy can potentially avoid or effectively mitigate upcoming outages. This thesis presents an energy management system (EMS) for such microgrids. The EMS uses a predictive approach to set operational schedules in order to (a) prolong the supply to critical system loads and (2) minimize the chances and duration of system-wide outages, specifically through pre-emptive load shedding. Online weather forecast data has been combined with the PV system model to assess potential energy production over a 48 hour period. These predictions, along with load forecasts and a model of the energy storage system, are used to predict the state-of-charge of the storage devices and characterize potential power shortages. Pre-emptive load shedding is subsequently planned and executed to avert outages or minimize the duration of unavoidable outages. A bounding technique has also been proposed to account for uncertainties in estimates of the stored energy. The EMS has been implemented using an event-driven framework with network communication. The approach has been validated through simulations and experiments using recorded real-world solar irradiance data. The results show that the outage durations have been reduced by a factor of 87% to 100% for an example operating scenario, selected to demonstrate the features of the scheme. The impact of uncertainties in the prediction models has also been investigated, specifically for the PV system rating and the battery capacity. A technique has been developed to compensate for such uncertainties by analyzing the data streams from the source and storage units. The technique is applied to the developed EMS strategy, where it is able to shorten the total outage duration by a factor of 12% over a 42-day scenario exhibiting a variety of irradiance conditions

Energy Management Strategies in hydrogen Smart-Grids: A laboratory experience

As microgrids gain reputation, nations are making decisions towards a new energetic paradigm where the centralized model is being abandoned in favor of a more sophisticated, reliable, environmentally friendly and decentralized one. The implementation of such sophisticated systems drive to find out new control techniques that make the system “smart”, bringing the Smart-Grid concept. This paper studies the role of Energy Management Strategies (EMSs) in hydrogen microgrids, covering both theoretical and experimental sides. It first describes the commissioning of a new labscale microgrid system to analyze a set of different EMS performance in real-life. This is followed by a summary of the approach used towards obtaining dynamic models to study and refine the different controllers implemented within this work. Then the implementation and validation of the developed EMSs using the new labscale microgrid are discussed. Experimental results are shown comparing the response of simple strategies (hysteresis band) against complex on-line optimization techniques, such as the Model Predictive Control. The difference between both approaches is extensively discussed. Results evidence how different control techniques can greatly influence the plant performance and finally we provide a set of guidelines for designing and operating Smart Grids.Ministerio de Economía y Competitividad DPI2013-46912-C2-1-

Optimal Energy Scheduling of Grid-connected Microgrids with Battery Energy Storage

The coupling of small-scale renewable-based energy sources, such as photovoltaic systems, with residential battery energy storages forms clusters of local energy resources and customers, which can be represented as controllable entities to the main distribution grid. The operation of these clusters is similar to that of grid-connected microgrids. The future distribution grid of multiple grid-connected microgrids will require proper coordination to ensure that the energy management of the microgrid resources satisfies the targets and constraints of both the microgrids’ and the main grid’s operation. The link between the battery dispatch and the induced battery degradation also needs to be better understood to implement energy management with long-term economic benefits. This thesis contributes to the solution of the above-mentioned issues with an energy management model developed for a grid-connected microgrid that uses battery energy storage as a flexible energy resource. The performance of the model was evaluated in different test cases (simulations and demonstrations) in which the model optimized the schedule of the microgrid resources and the energy exchange with the connected main grid, while satisfying the constraints and operational objectives of the microgrid. Coordination with the distribution system operator was proposed to ensure that the microgrid energy scheduling solution would not violate the constraints of the main grid.Two radial distribution grids were used in simulation studies: the 12-kV electrical distribution grid of the Chalmers University of Technology campus and a 12.6-kV 33-bus test system. Results of the Chalmers’ test case assuming the operation of two grid-connected microgrids with battery energy storage of 100-200 kWh showed that the microgrids’ economic optimization could reduce the cost for the distribution system operator by up to 2%. Coordination with the distribution system operator could achieve an even higher reduction, although it would lead to sub-optimal solutions for the microgrids. Application of decentralized coordination showed the effectiveness of utilizing microgrids as flexible entities, while preserving the privacy of the microgrid data, in the simulations performed with the 33-bus test system. The developed microgrid energy management model was also applied for a building microgrid, where the battery energy storage was modeled considering both degradation and real-life operation characteristics derived from measurements conducted at real residential buildings equipped with stationary battery energy storages. Simulation results of a building microgrid with a 7.2 kWh battery energy storage showed that the annual building energy and battery degradation cost could be reduced by up to 3% compared to when the impact of battery degradation was neglected in the energy scheduling. To demonstrate the model’s practical use, it was integrated in an energy management system of the real buildings, where the buildings’ battery energy storages and, by extent, their energy exchange with the main grid, were dispatched based on the model’s decisions in several test cases.The test cases’ results showed that the model can reduce the energy cost of the microgrid both in short-term and in long-term. Moreover, with the help of this model, the microgrid can be employed as a flexible resource and reduce the operation cost of the main distribution grid

Propagating uncertainty in tree-based load forecasts

This paper discusses the use of ensembles of regression trees as a straightforward but versatile methodology to generate short term (day-ahead) load forecasts for real data from the Global Energy Forecasting Competition 2014. Since temperature is a strong predictor of load, we investigate how forecast uncertainty in temperature can affect the performance of the prediction model. To this end, a singular value decomposition (SVD) based approach is harnessed to simulate noisy but realistic temperature profiles. Our results show that as long as uncertainty is not exceedingly large, it is worthwhile to include temperature forecasts as predictors