Optimization Under Uncertainty in Building Energy Management

The introduction of decentralized energy resources as well as energy storage systems to the energy system calls for new control and coordination mechanisms and systems. This is also true for buildings. An optimized operation of buildings comprising decentralized generation and energy storage systems can be achieved by a building energy management system. It controls and coordinates the operation of individual devices in a building\u27s energy system to achieve given goals, such as the increase of energy efficiency, the decrease of carbon emissions, the minimization of operating costs or the provision of demand response measures. This thesis picks up on this idea and extends the ongoing research by presenting an approach to the optimized operation of building energy systems that includes the uncertainties in the predictions of the future energy generation and consumption into the control scheme of a building energy management system. To do so, this thesis identified the use of a scenario-based consideration of the uncertainties to be best suited. The presented approach uses a rolling horizon optimization approach with a stochastic two-stage optimization problem, which considers several forecast scenarios in the optimization

A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads

This paper aims to present the significance of predicting stochastic loads to improve the performance of a low voltage (LV) network with an energy storage system (ESS) by employing several optimal energy controllers. Considering the highly stochastic behaviour that rubber tyre gantry (RTG) cranes demand, this study develops and compares optimal energy controllers based on a model predictive controller (MPC) with a rolling point forecast model and a stochastic model predictive controller (SMPC) based on a stochastic prediction demand model as potentially suitable approaches to minimise the impact of the demand uncertainty. The proposed MPC and SMPC control models are compared to an optimal energy controller with perfect and fixed load forecast profiles and a standard set-point controller. The results show that the optimal controllers, which utilise a load forecast, improve peak reduction and cost savings of the storage device compared to the traditional control algorithm. Further improvements are presented for the receding horizon controllers, MPC and SMPC, which better handle the volatility of the crane demand. Furthermore, a computational cost analysis for optimal controllers is presented to evaluate the complexity for a practical implementation of the predictive optimal control systems

Stochastic optimal energy management system for RTG cranes network using genetic algorithm and ensemble forecasts

In low voltage networks, Energy Storage Systems (ESSs) play a significant role in increasing energy cost savings, peak reduction and energy efficiency whilst reinforcing the electrical network infrastructure. This paper presents a stochastic optimal management system based on a Genetic Algorithm (GA) for the control of an ESS equipped with a network of electrified Rubber Tyre Gantry (RTG) cranes. The stochastic management system aims to improve the reliability and economic performance, for given ESS parameters, of a network of cranes by taking into account the uncertainty in the RTGs electrical demand. A specific case study is presented using real operational data of the RTGs netwrok in the Port of Felixstowe, UK, and the results of the stochastic control system is compared to a standard set-point controller. In this paper, two forecast data sets with different levels of accuracy are used to investigate the impact of the crane demand forecast error in the proposed ESS control system. The results of the proposed control strategies indicate that the stochastic management system successfully increases the electric energy cost savings, the peak demand reductions and successfully outperforms a comparable set-point controller

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

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

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

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

Fast Solution Techniques for Energy Management in Smart Homes

In the future, residential energy users will seize the full potential of demand response schemes by using an automated smart home energy management system (SHEMS) to schedule their distributed energy resources. The underlying optimisation problem facing a SHEMS is a sequential decision making problem under uncertainty because the states of the devices depend on the past state. There are two major challenges to optimisation in this domain; namely, handling uncertainty, and planning over suitably long decision horizons. In more detail, in order to generate high quality schedules, a SHEMS should consider the stochastic nature of the photovoltaic (PV) generation and energy consumption. In addition, the SHEMS should accommodate predictable inter-daily variations over several days. Ideally, the SHEMS should also be able to integrate into an existing smart meter or a similar device with low computational power. However, extending the decision horizon of existing solution techniques for sequential stochastic decision making problems is computationally difficult and moreover, these approaches are only computationally feasible with a limited number of storage devices and a daily decision horizon. Given this, the research investigates, proposes and develops fast solution techniques for implementing efficient SHEMSs. Specifically, three novel methods for overcoming these challenges: a two-stage lookahead stochastic optimisation framework; an approximate dynamic programming (ADP) approach with temporal difference learning; and a policy function approximation (PFA) algorithm using extreme learning machines (ELM) are presented. Throughout the thesis, the performance of these solution techniques are benchmarked against dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) using a range of residential PV-storage (thermal and battery) systems. We use empirical data collected during the Smart Grid Smart City project in New South Wales, Australia, to estimate the parameters of a Markov chain model of PV output and electrical demand using an hierarchical approach, which first cluster empirical data and then learns probability density functions using kernel regression (Chapter 2). The two-stage lookahead method uses deterministic MILP to solve a longer decision horizon, while its end-of-day battery state of charge is used as a constraint for a daily DP approach (Chapter 4). Here DP is used for the daily horizon as it is shown to provide close-to-optimal solutions when the state, decision and outcome spaces are finely discretised (Chapter 3). However, DP is computationally difficult because of the dimensionalities of state, decision and outcome spaces, so we resort to MILP to solve the longer decision horizon. The two-stage lookahead results in significant financial benefits compared to daily DP and stochastic MILP approaches (8.54% electricity cost savings for a very suitable house), however, the benefits decreases as the actual PV output and demand deviates from their forecast values. Building on this, ADP is proposed in Chapter 5 to implement a computationally efficient SHEMS. Here we obtain policies from value function approximations (VFAs) by stepping forward in time, compared to the value functions obtained by backward induction in DP. Similar to DP, we can use VFAs generated during the offline planning phase to generate fast real-time solutions using the Bellman optimality condition, which is computationally efficient compared to having to solve the entire stochastic MILP problem. The decisions obtained from VFAs at a given time-step are optimal regardless of what happened in the previous time-steps. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 57.27% for a most suitable home, with low computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort. However, ADP still takes a considerable amount of time to generate VFAs in the off-line planning phase and require us to estimate PV and demand models. Given this, a PFA algorithm that uses ELM is proposed in Chapter 6 to overcome these difficulties. Here ELM is used to learn models that map input states and output decisions within seconds, without solving an optimisation problem. This off-line planning process requires a training data set, which has to be generated by solving the deterministic SHEMS problem over couple of years. Here we can use a powerful cloud or home computer as it is only needed once. PFA models can be used to make fast real-time decisions and can easily be embedded in an existing smart meter or a similar low power device. Moreover, we can use PFA models over a long period of time without updating the model and still obtain similar quality solutions. Collectively, ADP and PFA using ELM can overcome challenges of considering the stochastic variables, extending the decision horizon and integrating multiple controllable devices using existing smart meters or a device with low computational power, and represent a significant advancement to the state of the art in this domain