Iterative Adaptive Dynamic Programming Adviced Campus Scale Social Energy System Management Demand side management (DSM) schemes are becoming more and more sophisticated in modern power systems, in which energy consumption should be properly planned and controlled, together with social elements such as consumer experience, environmental influences, and management policy and rules. In this paper, inspired by the concept of xe2x80x9csocial energyxe2x80x9d, we incorporate user experience evaluation, realtime electricity price calculation and energy consumption prediction into a unified campusscale energy management system, to investigate the interactions among energy and social elements. The University of Denveru0027s campus grid is used as the test bench, and energy consumption data were collected from 6 oncampus buildings to conduct the numerical experiments. Specifically, to deal with the xe2x80x9coptimizationoveroptimizationxe2x80x9d problem introduced by realtime pricing, and to tackle the high complexity problem brought by dynamic building consumption patterns, we implement neural network based iterative adaptive dynamic programming algorithm in this complex system to solve for the best overall system social cost. Experiment results show that the proposed algorithm is able to provide reasonable energy management advice in a practical sociotechnical energy system.

Consumption Optimization in an Office Building Considering Flexible Loads and User Comfort This paper presents a multiperiod optimization algorithm that is implemented in a Supervisory Control and Data Acquisition system. The algorithm controls lights and air conditioners as flexible loads to reduce the consumption and controls a dishwasher as a deferrable load to implement the load shifting. Several parameters are considered to implement the algorithm for several successive periods in a real building operation. In the proposed methodology, optimization is done regarding user comfort, which is modeled in the objective function related to the indoor temperature in each room, and in the constraints in order to prevent excessive power reduction, according to usersxe2x80x99 preferences. Additionally, the operation cycle of a dishwasher is included, and the algorithm selects the best starting point based on the appliance weights and power availability in each period. With the proposed methodology, the building energy manager can specify the moments when the optimization is run with consideration of the operational constraints. Accordingly, the main contribution of the paper is to provide and integrate a methodology to minimize the difference between the actual and the desired temperature in each room, as a measure of comfort, respecting constraints that can be easily bounded by building users and manager. The case study considers the real consumption data of an office building which contains 20 lights, 10 ACs, and one dishwasher. Three scenarios have been designed to focus on different functionalities. The outcomes of the paper include proof of the performance of the optimization algorithm and how such a system can effectively minimize electricity consumption by implementing demand response programs and using them in smart grid contexts.