Development and validation of an intelligent load control algorithm

Abstract

The renewable generation technologies form a significant (>20%) fraction of grid capacity, however their generation capabilities remain variable in nature. Therefore, utilities will be forced to maintain a significant standby capacity to mitigate the imbalance between supply and demand. Because more than 75% of electricity consumption occurs in buildings, building loads can be used to mitigate some of the imbalance. This paper describes the development and validation of an intelligent load control (ILC) algorithm that can be used to manage loads in a building or group of buildings using both quantitative and qualitative criteria. ILC uses an analytic hierarchy process to prioritize the loads for curtailment. The ILC process was developed and tested in a simulation environment to control a group of rooftop units (RTUs) to manage a building's peak demand while still keeping zone temperatures within acceptable deviations. The ILC algorithm can be implemented at a low cost on a supervisory controller without the need for additional sensing. By anticipating future demand, the process can be extended to add advanced control features such as precooling and preheating to alleviate comfort when operation of the RTUs is curtailed to manage the peak demand.

Alienation Control Technology of Information Pushed by Intelligent Algorithms in Media Communication

“…By anticipating future demand, the process can be extended to add advanced controls to ease comfort while reducing RTU operation to manage peak demand. However, it consumes a lot of energy [ 3 ]. To bridge the time during GNSS outages, Zhang and Wang propose a novel hybrid intelligence algorithm that combines a discrete grey predictor (DGP) and a multilayer perceptron (MLP) neural network (DGP-MLP).…”

Control-Centric Living Laboratory for Management of Distributed Energy Resources

“…The ILC algorithm manages loads using an analytic hierarchy process (AHP) by dynamically prioritizing the available loads for curtailment. Detailed formulation and use cases of the ILC are demonstrated in [45] and [41]. ILC has three basic elements: a goal, criteria, and alternatives.…”

Buildings Participation in Resilience Enhancement of Community Microgrids: Synergy Between Microgrid and Building Management Systems

“…∅ 𝑖,ℎ 𝐿 determines the weight factor for the ℎ 𝑡ℎ load group in the building connected to 𝑖 𝑡ℎ bus. Based on the normalized matrix, ∅ the sum of all elements in the matrix should equal 1, otherwise represented as ∑ ∅ 𝑖,ℎ 𝐿 𝑖,ℎ = 1 [31]. The problem is formulated as a MILP model that can be effectively solved by commercial solvers.…”

“…Considering the disintegration of loads into different groups, the load groups can be shed by (31): (31) where 𝑥 𝑖,ℎ 𝐿 is the controllable factor of the h th group load as a percentage of the entire building, and the sum is less than 1, signifying that the entire building cannot have controllable loads. Once all buildings maintain their power consumption less or equal to the limit set by the MGMS, the microgrid's stable and safe operation is ensured.…”