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

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

Energy Scheduling Strategies for Grid-connected Microgrids: A Case Study on Chalmers Campus

This paper focuses on the optimal energy managementof grid-connected microgrids with battery energy storagesystems. The microgrid energy management and the optimalpower flow of the distribution network are formulated as mixed-integerlinear optimization problems to evaluate microgrid energyscheduling strategies including cost minimization, maximum useof own resources, and minimum energy exchange with theupstream network. The real distribution network of ChalmersUniversity of Technology campus is used as a case study. Thestudy results show that economic optimization yields an annualmicrogrid cost reduction of 4%. Alternatively, if the microgridminimizes the energy exchange, virtual islanding operation (zeroenergy exchange) for 3211 hours can be achieved within a year.The results also present the effects on the operation and costof the distribution system and highlight a trade-off betweenmicrogrid cost minimization and battery lifetime

Scenario-based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers

Market-based Energy Management Model of a Building Microgrid Considering Battery Degradation

This paper presents a model for energy management system of a building microgrid coupled with a battery energy storage. The model can be used to dispatch the battery as a flexible energy resource using a market-based setting. The battery is modeled considering battery degradation and real-life operation characteristics derived from measurements at a residential building. The performance of the model was evaluated first with simulations and integrated afterwards to an energy management system, which was demonstrated at a real residential building (HSB Living Lab) equipped with photovoltaic and battery storage systems. The simulation results showed that the building owner, and subsequently the residents, could benefit from the proposed model in reduced annual cost up to 3.1% under the considered pricing scheme. The demonstration results showed that dispatch under the measurement-based model could decrease the undelivered energy over the daily requested amount from the battery from 13.3% to 3.7%. Thus, the proposed model, which couples the measurement-based dispatch with battery degradation, can lead to a more accurate estimation of the building operation cost and an improved overall performance of battery as a flexible resource in building microgrids

Values and Impacts of Incorporating Local Flexibility Services in Transmission Expansion Planning

This paper presents a cost-based TSO-DSO coordination model to quantify the value of local flexibility services and analyze its impact on the transmission grid expansion and the system operation. Flexibility is provided to the DC power flow transmission grid model by microgrids within the integrated AC power flow distribution grid model. The model\u27s objective is to minimize the overall cost of transmission investments and procured flexibility and is achieved using a bilevel optimization approach where the power exchanges on all connected grid interfaces are controlled. Case studies using a combined test system of the IEEE RTS-96 transmission network interfacing multiple 33-bus distribution grids were performed to validate the model and assess the values and impacts of local flexibility on the transmission system expansion. The results showed that the proposed model modified the investment plan and dispatch of flexibility resources reducing the investment and operation cost of the transmission system

Chalmers Campus as a Testbed for Intelligent Grids and Local Energy Systems

This paper presents an overview of a testbed for intelligent distribution grids, local energy systems, and energy flexible buildings, which is being developed at the campus of Chalmers University of Technology in Gothenburg, Sweden. It describes the test sites, the functionalities, and the planned demonstration activities within the scope of on-going research projects. The proposed demonstrations include a local energy market platform, energy management solutions for microgrids and smart buildings, as well as voltage control in distribution grids. The paper aims to show how the physical energy supply systems of the university are being adapted to integrate the communication and control set-ups that provide the technical requirements for smart grid interoperability. As an example, the on-site implementation of remote battery control is presented, where initial results show the feasibility and potential benefits of the external control. Finally, challenges and lessons learned during the development of the testbed are highlighted

Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a grid-connected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab)showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid\u27s total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid\u27s peak power cost as shown by its 20.2% increase in comparison to the ideal case

Effect of Short-term and High-resolution Load Forecasting Errors on Microgrid Operation Costs

The aim of this paper is to evaluate the effect of the load forecasting errors to the operation costs of a grid-connected microgrid. To this end, a microgrid energy scheduling optimization model was tested with deterministic and stochastic formulations under two solution approaches i.e., day-ahead and rolling horizon optimization. In total, twelve simulation test cases were designed receiving as input the forecasts provided by one of the three implemented machine learning models: linear regression, artificial neural network with backpropagation, and long short-term memory. Simulation results of the weekly operation of a real residential building (HSB Living Lab)showed no significant differences among the costs of the test cases for a daily mean absolute percentage forecast error of about 12%. These results suggest that operators of similar microgrid systems could use simplifying approaches, such as day-ahead deterministic optimization, and forecasts of similar, non-negligible accuracy without substantially affecting the microgrid\u27s total cost as compared to the ideal case of perfect forecast. Improving the accuracy would mainly reduce the microgrid\u27s peak power cost as shown by its 20.2% increase in comparison to the ideal case