Short-term Self-Scheduling of Virtual Energy Hub Plant within Thermal Energy Market

Multicarrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among multiple energy systems and energy hubs in different energy markets. By the advent of the local thermal energy market in many countries, energy hubs' scheduling becomes more prominent. In this article, a new approach to energy hubs' scheduling is offered, called virtual energy hub (VEH). The proposed concept of the energy hub, which is named as the VEH in this article, is referred to as an architecture based on the energy hub concept beside the proposed self-scheduling approach. The VEH is operated based on the different energy carriers and facilities as well as maximizes its revenue by participating in the various local energy markets. The proposed VEH optimizes its revenue from participating in the electrical and thermal energy markets and by examining both local markets. Participation of a player in the energy markets by using the integrated point of view can be reached to a higher benefit and optimal operation of the facilities in comparison with independent energy systems. In a competitive energy market, a VEH optimizes its self-scheduling problem in order to maximize its benefit considering uncertainties related to renewable resources. To handle the problem under uncertainty, a nonprobabilistic information gap method is implemented in this study. The proposed model enables the VEH to pursue two different strategies concerning uncertainties, namely risk-averse strategy and risk-seeker strategy. For effective participation of the renewable-based VEH plant in the local energy market, a compressed air energy storage unit is used as a solution for the volatility of the wind power generation. Finally, the proposed model is applied to a test case, and the numerical results validate the proposed approach

Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Microgrid design, control, and performance evaluation for sustainable energy management in manufacturing

This research studies the capacity sizing, control strategies, and performance evaluation of the microgrids with hybrid renewable sources for manufacturing end use customers towards a distributed sustainable energy system paradigm. Microgrid technology has been widely investigated and applied in commercial and residential sector, while for manufacturers, it has been less explored and utilized. To fill the gap, the dissertation first proposes a cost-effective sizing model to identify the capacities as well as control strategies of the components in microgrids considering a commonly used energy tariff, i.e., Time of Use (TOU). Then, the sizing model is extended by integrating control strategies for both microgrid components and manufacturing systems considering a typical demand response program, i.e., Critical Peak Pricing (CPP), where customer side load adjustment is highly encouraged. After that, the control strategy of the manufacturers in an overgeneration mitigation-oriented demand response program is further investigated based on the identified optimal size of onsite microgrid to minimize the energy cost. Later, the system is analyzed from its higher level of abstraction where a prosumer community is developed by aggregating such manufacturers with onsite microgrid system. To enhance the reliable energy operation in the community, the performance of the microgrid is investigated through the estimation of the lifetime of Battery Energy Storage System (BESS), a critical design parameter the architecture. Finally, conclusions are presented and future research on real-time joint control strategy for both microgrids and manufacturing systems and identification as well as optimal energy management of the controllable loads in manufacturing system are discussed --Abstract, page iii

Towards Smarter Electric Vehicle Charging with Low Carbon Smart Grids: Pricing and Control.

Environmental and political directions indicate transition to a decarbonized transportation system is necessary as it is one of the most pollutant sectors regarding greenhouse gas emissions. Research in Demand Side Management suggests that its tools are the most cost-effective option for improving the performance of the grid without incurring into high infrastructure investments, hence reducing the payback for start-ups in the sector. This Thesis proposes solutions to tackle 5 objectives around this area of research: 1-2 are related to developing a demand response pricing and EV smart charging strategies, 3-4 are related to developing a multi-objective charging scheme in order to ensure fairness and reduction of CO2eq emissions, and 5 is related to testing parameters of EV charging to understand future improvements and limitations in the proposed models. Chapter 3, that tackles objectives 1-2, proposes a data-driven optimisation algorithm with pricing and control modules that communicate with each other to achieve a successful integration with the grid by charging at the right price and expected time. The results show customers can be positively engaged with pricing signals while providing support to the grid. Chapter 4, which tackles objectives 3-4, proposes a multi-objective EV charging formulation that include perspectives of EV users, a carbon regulator and a charging station operator. The multi-objective formulation is solved with a genetic algorithm in order to find the fairest and the greenest solution. Results which are evaluated using different scenarios show different weights to each objective function can differ based on the charging location and EV charging availability. Finally, Chapter 5 which tackles objective 5, shows a sensitivity analysis where improvements in revenues, reduction of carbon emissions and bidding capacity depend on the evaluation of EV users’ parameters, and the charging station control and sizing

Predictive Demand Response Modeling for Logistic Systems Innovation and Optimization

In the ever-increasing dynamics of global business markets, logistic systems must optimize the usage of all possible sources to continually innovate. Scenario-based demand prediction plays an important role in the effective economic operations and planning of logistics. However, many uncertainties and demand variability, which are associated with innovative changes, complicate demand forecasting and expose system operators to the risk of failing to meet demand. This dissertation presents new approaches to predictively explore how customer preferences will change and consequently demand would respond to the new setup of services caused by an innovative transformation of the logistic layout. The critical challenge is that the responses from customers in particular and demand in general to the innovative changes and corresponding adjustments are uncertain and unknown in practice, and there is no historical data to learn from and directly support the predictive model. In this dissertation, we are dealing with three different predictive demand response modeling approaches, jointly shaping a new methodological pathway. Chapter 1 provides a novel approach for predictive modeling probabilistic customer behavior over new service offers which are much faster than ever done before, based on the case of a large Chinese parcel-delivery service provider. Chapter 2 introduces an approach for predicting scenario-based erection-site demand schedules under uncertainty of disruptive events in construction projects whose logistics transformed from traditional to modular style, based on the case of a USA-based innovative leader in modular building production. For such a leader to advance in its logistics design innovations and associated capacity adjustments, and also to enhance its capability for taking more market share, it is crucial to estimate potential future demand for modular construction and corresponding probable projects in terms of their potential location, size, and characteristics. For this purpose, Chapter 3 introduces a methodological approach for estimating scenario-based future demand for modular construction projects to be implemented over the US metropolitan statistical areas.Ph.D

Low carbon multi-vector energy systems: a case study of the University of Edinburgh's 2040 'Net Zero' target

The ultimate goal of this research was to develop a methodology to support decision-making by large (public sector) organisations regarding future energy technology choices to reduce carbon emissions. This culminated in the development of a multi-vector campus energy systems modelling tool that was applied to the University of Edinburgh as a case study. To deliver this a series of objectives were addressed. Machine learning models were applied to model building heat and electrical energy use for extrapolation to campus level. This was applied to explore the scope to reduce campus level emissions through operational changes; this demonstrated that it is difficult to further reduce the carbon emissions without technological changes given the University’s heavy reliance on natural gas-fired combined heat and power and boilers. As part of the analysis of alternative energy sources, the scope for off-campus wind farms was considered; specifically this focussed on estimation of wind farm generation at the planning stage and employed a model transfer strategy to facilitate use of metered data from wind farms. One of the key issues in making decisions about future energy sources on campus is the simultaneous changes in the wider energy system and specifically the decarbonisation of electricity; to facilitate better choices about onsite production and imports from the grid, a fundamental electricity model was developed to translate the National Grid Future Energy Scenarios into plausible patterns of electricity prices. The learning from these activities were incorporated into a model able to develop possible configurations for campus-level multi-vector energy systems given a variety of future pathways and uncertainties. The optimal planning model is formulated as a mixed-integer linear programming model with the objective to minimize the overall cost including carbon emissions. A numerical case study for the planning of three real-world campuses is presented to demonstrate the effectiveness of the proposed method. The conclusion highlights the importance of energy storage and a remote wind farm in these energy systems. Also, it is noted that there is no single solution that works in all cases where there are differences in factors such as device cost and performance, the gap between gas and electricity prices, weather conditions and the use (or otherwise) of cross-campus local energy balancing

Climate Adaptation and Resilience Across Scales

Climate Adaptation and Resilience Across Scales provides professionals with guidance on adapting the built environment to a changing climate. This edited volume brings together practitioners and researchers to discuss climate-related resilience from the building to the city scale. This book highlights North American cases that deal with issues such as climate projections, public health, adaptive capacity of vulnerable populations, and design interventions for floodplains, making the content applicable to many locations around the world. The contributors in this book discuss topics ranging from how built environment professionals respond to a changing climate, to how the building stock may need to adapt to climate change, to how resilience is currently being addressed in the design, construction, and operations communities. The purpose of this book is to provide a better understanding of climate change impacts, vulnerability, and resilience across scales of the built environment. Architects, urban designers, planners, landscape architects, and engineers will find this a useful resource for adapting buildings and cities to a changing climate