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

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

Evaluating microgrid effectiveness in transitioning energy portfolios

Microgrid energy systems have emerged as a potential solution to rising greenhouse gas emissions from dependence on fossil fuels. This research provides a framework for evaluating the utility of microgrids. Three key findings are presented: use of a state-of-the-art matrix (SAM) analysis to identify gaps in key research areas that inhibit wide-spread microgrid adoption, development of a system dynamics (SD) model, and a cost benefit analysis case study to evaluate microgrid feasibility in partially meeting the energy demand of a building. Governments play a central role in developing clean energy strategies. A SAM was developed to determine if key microgrid barriers to adoption defined by a state government were being addressed. The results of the study suggest that environmental and sustainability benefits had not been sufficiently addressed. Using the SAM findings, an SD model was used to evaluate the environmental and sustainability benefits of transitioning a state\u27s residential electricity portfolio. The SD model outputs suggest that fossil fuel depletion and greenhouse gas emissions would be reduced, but the financial investment would be significant. Lastly, a cost benefit analysis was conducted on a microgrid partially meeting the energy demand of a university campus building. The results demonstrated that selection of a proper discount factor and recognition of useful life are critical success factors for microgrid energy projects. Collectively, these findings provide the engineering manager with a method to evaluate the feasibility of proposed microgrid projects, the city planner with the system-level implications of a large-scale energy transition project, and the policy maker with the necessary information to develop policies that promote a clean energy future --Abstract, page iv

Forbrukerfleksibilitet i kraftmarkeder

Demand flexibility integration is an important measure for the decarbonization of energy systems and a more efficient use of resources. Demand flexibility can provide multiple benefits to the power system and reduce system costs. Adjusting electricity demand to match variable production supports the integration of larger shares of variable renewable energy (VRE). Using demand response for system services provided by network operators can contribute to a more cost-efficient use of infrastructure and resources. Demand flexibility is a large and complex field of study which includes different markets, different grid voltage levels and different actors. The aim of this PhD project is to study how demand flexibility can be optimally integrated into electricity markets, taking account of the benefits to the power system as a whole and the interplay between different markets. Demand flexibility is studied from the perspective of the whole system, as well as from the private economic perspective of aggregators and electricity consumers. The thesis includes separate studies which go in depth about specific topics. The whole system perspective is studied in Paper I, which focuses on the value of demand flexibility in spot and reserve markets in power systems with high shares of VRE. The perspective of TSO and DSO is studied in Paper II, which proposes a marketplace for procurement of transmission and distribution system services from demand flexibility. The perspective of demand flexibility aggregator is studied in Paper III which develops an optimization framework for an aggregator participating in the wholesale and the regulation capacity markets. The perspective of private electricity consumers is studied in Paper IV which studies price-based demand response and investments in load control in an energy system. The results of these studies offer various useful insights. Firstly, demand flexibility was found to significantly decrease the system cost when large shares of VRE are integrated into the system. This happens primarily by replacing reserve provision from coal and gas plants but also by reducing peak load generation due to price response on the wholesale market. Optimal allocation of demand flexibility between reserve and wholesale markets maximizes the system benefits. The results suggest that in systems with large shares of VRE and small shares of base load, more demand flexibility should be placed in the reserve market than in the wholesale power market. Demand flexibility also benefits the distribution system, and it was also found that new market designs and better coordination between the transmission and distribution levels are important for efficiently integrating demand flexibility and minimizing the total procurement costs. New market designs can ensure that demand flexibility is used to maximize the value for the whole system and not only for single actors. Next, the results of the studies illustrate that demand flexibility access to many markets is beneficial, from both the system and private economic perspectives. It increases the value of demand flexibility, gives incentives to aggregators’ business and ensures that demand flexibility is optimally allocated between markets based on price. However, market interplay can also have negative effects, as when demand flexibility providers favour one particular market with higher profitability and flee from other markets. New market designs for demand flexibility should consider the interplay between different markets. Finally, modelling demand response to electricity price shows that private investments in demand flexibility are governed by the cost of load control, the daily electricity price variability and the price flattening effect. The price flattening effect implies that demand response to price reduces price volatility in the market, and at some point, no more demand response is feasible. To achieve this optimal demand response level in the wholesale market, it is important to have correct feedback between the market and consumers so that they do not respond more is optimal from the system perspective. To sum up, the results of this PhD research suggest that efficient integration of demand flexibility into electricity markets implies giving it access to many markets, strengthening the role of aggregators, improving coordination between the distribution and transmission system levels and promoting market designs that optimize demand flexibility use and system value. This thesis illustrates the importance of studying demand response in a holistic perspective, including different markets, actors and system levels.Norwegian Research Council ; Enfo ; Sysco ; NV