Energy management for user’s thermal and power needs: A survey <s> Discrete vs. continuous models <s> This paper considers heat and power production from micro Combined Heat and Power systems and heat storage in a network of households. The goal is to balance the local heat demand and supply in combination with balancing the power supply and demand in the network. The on-off decisions of the local generators are done completely distributed based on local information and information exchange with a few neighbors in the network. This is achieved by using an information sharing model with a distributed model predictive control method based on dual-decomposition and sub-gradient iterations. Because of the binary nature of the decisions, a mixed integer quadratic problem is solved at each agent. The approach is tested with simulation, using realistic heat and power demand patterns. We conclude that the distributed control approach is suitable for embedding the distributed generation at a household level. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Discrete vs. continuous models <s> Intermittent generation is challenging for the existing power systems, which have difficulties in following varying electricity production. To help the generation and demand be in balance, demand response (DR) provides one promising approach. Among many DR applications, residential micro-CHP systems can be advantageous, as these systems typically comprise storage in addition to a controllable cogeneration unit. Current state-of-the-art control strategies for residential micro-CHP systems utilize time-varying electricity price to optimally operate the system. Thus, such systems have the ability to export or import electricity when either one is beneficial. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Discrete vs. continuous models <s> PV installations in buildings can utilize different on-site flexibility resources to balance mismatch in electricity production and demand. This paper studies cost-optimal and rule-based control for buildings with PV, employing a heat pump, thermal and electrical storage and shiftable loads as flexibility sources to increase the value of PV for the prosumer. The cost-optimal control minimizes variable electricity cost employing market data on electricity price and optionally constrains grid feed-in to zero; the rule-based control aims at maximizing PV self-consumption. The flexibility strategies are combined into a simulation model to analyze different system configurations over a full year. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Discrete vs. continuous models <s> Along with the development of Smart Energy System (SES), the advancing popularity of hybrid energy appliances, such as micro-combined heat and power (CHP) and electric heaters, requires an overall energy management strategy to optimize the energy using while guaranteeing energy supply for both the electricity and heat demand. This paper based on the concept of Multi-Commodity Smart Energy System (MC-SES) proposes a self-sufficient hybrid energy model to minimize the energy exchange with the external electricity grid. Next to the optimization solution for this hybrid energy management problem, a greedy algorithm has been proposed to alleviate the high computational complexity of optimization searching. The optimization, direct allocation and greedy-based methods will all be analyzed, simulated and compared in this paper. <s> BIB004
All reviewed studies consider discrete models, that means, state variables change at regularly intervaled, countable points in time. Thus, the system is described by the changing in state at those points in time. It can vary between one minute (e.g., BIB001 and one hour (e.g., BIB002 . Alternatively, the number of states is considered infinite, and the system evolves continuously and not abruptly from one state to another. Some studies, e.g., BIB003 , BIB004 , propose a continuous formulation of the scheduling problem. Continuous solutions are then discretized for the actual implementation.