Energy management for user’s thermal and power needs: A survey <s> Load model 4.5.1. Aggregated vs per appliance <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> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Load model 4.5.1. Aggregated vs per appliance <s> Abstract This paper proposes a new optimization model for Smart Home Management Systems (SHMS) in order to increase the profits of Load Serve Entities (LSEs) and customers from technical and financial points of view. In the recent decades, performing Demand Response (DR) is one of the most efficient ways to improve the performance of power distribution systems in terms of power loss, and investment costs. The LSEs can implements some strategies like offering incentives to customers to change their consumption pattern with the aim of reducing power loss, improving asset management and increasing the profits. On the other hand, the end users can participate in DR programs to decrease electricity bills and earn monetary incentives from LSEs proportionate to their contributions to the energy loss reduction. In this paper, Distribution Locational Marginal Price (DLMP) instead of time-based pricing mechanism is applied to bill the customers. In the proposed strategy, the energy bill of customers and power loss of the system are simultaneously decreased. For dealing with uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The operation of household resources and appliances are optimized through a Mixed Integer Linear Programming (MILP), which has a two-stage stochastic model. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost. Besides, the superiority of DLMP and the proposed method over existing pricing mechanism is demonstrated. <s> BIB002
The power demand can be considered either as an aggregated load profile or as a combination of appliances, see Table 6 . In the former case, how the single appliances contribute to shape the load profile is not further investigated. The profile can be composed of different shares, namely uncontrollable, programmable, and curtailable loads, depending on the control strategy that can be implemented (e.g.,   BIB002 BIB001 . Another group of studies considers the contribution of different appliances to the final power demand, given their parameters, such as rated power, time window for its operation, duration of operation, and total energy consumption, often in combination with an aggregated uncontrollable load profile. When this approach is taken, it can be assumed the user sets some of these parameters, according to his own preferences.