Restricted Neighborhood Communication Improves Decentralized Demand-Side Load Management

Abstract

Abstract-We address demand-side management of dispatchable loads in a residential microgrid by means of decentralized controllers deployed in each household. Controllers simultaneously optimize two possibly conflicting objectives: minimization of energy costs for the end user (considering a known, timedependent tariff) and stabilization of the aggregate load profile (load flattening). The former objective can be optimized independently by each controller. On the other hand, the latter could benefit from a communication infrastructure that allows the controllers to explicitly exchange information and coordinate.To study how different levels of communication pervasiveness affect system performance, we developed a realistic microsimulation environment accounting for the behavior of residents, dispatchable and non-dispatchable household loads, and the effects on the distribution network. We considered a generic model of communication among household controllers, not tied to any specific technology, and based on the partitioning of the households in a number of groups (neighborhoods). Controllers within the same neighborhood enjoy full connectivity, but cannot interact with controllers outside of their neighborhood. Through extensive simulation experiments, we observed that even communication neighborhoods constituted by as few as 3-4 households are sufficient to effectively stabilize the aggregate network load profile, with minimal bandwidth consumption. Increasing the neighborhood size leads to comparatively negligible performance improvements. We conclude that effective load flattening can be achieved with minimal requirements of communication infrastructure and transmitted information.

A Novel Droop-Based Average Voltage Sharing Control Strategy for DC Microgrids

“…Equations (13) and (14) show that the droop gain reduces effective shunt capacitance; meanwhile, the virtual shunt resistance caused by the proportional controller increases. This exactly explains why increasing the droop gain obtains a higher resonance frequency (smaller shunt capacitance) and moves the real part of the cable mode toward RHP (larger shunt resistance).…”

A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid

“…Maximizes the consumers' payoff by improving the economic efficiency of the residential consumption [77] Maximizes the welfare of consumers [89] Maximizes consumers profit [93] PAPR reduces [95] Minimizes the operation cost [98] Reduces the consumers' electricity expenses [112] Cost and PAPR reduction Minimizes consumers' bills by up to 25% [76] Electricity bill reduction Minimizes the waiting time of the appliances [113] Minimizes consumers' electricity consumption [81] The total generation cost is minimized at the Nash equilibrium point [86] Reduces the bills by up to 22% [91] Minimizes the cost of energy [87] Electricity cost reduction Minimizes the total average cost of electricity of all consumers [114] Electricity cost reduction and stabilization of the load profile Minimizes electricity consumption of all consumers [115] Generation cost and PAPR minimization Minimizes the consumers' energy costs [62] Maximization of the overall utility Satisfies the budget limits [80] Maximization of the welfare of consumers Minimizes transmission losses [96] Maximization of the generation Maximizes the generation capacity [116] Maximization of the social welfare Minimizes the electricity costs which, in turn, maximizes the welfare [117] Maximization of the consumer profit Minimizes the consumers' electricity bills [118] Minimization of the generation cost Reduces the generation cost and demand during peak hours which flattens the demanded load profile [92] Minimization of the generation cost and demand during peak hours…”

Electric energy management in residential areas through coordination of multiple smart homes

“…End-users are charged based on the ratio of their individual consumption over the aggregated consumption. As a result, the aggregated electricity cost of the residential area is reduced from $44.77 to $37.90 for the simu- Multi-agent systems [94], [95], [96], [97] Game theory [98], [99], [100] Optimization techniques [85], [101], [102], [103], [104], [105] lation duration, and the PAR is decreased from 2.1 to 1.8.…”

“…In the literature, problems can take into account grid conditions (e.g., congestion) in the optimization. Formulations can also consider stochastic problems (e.g., with uncertainty on consumption [85] and renewable generation [101]), multiple objectives [102,103], or other objectives such as maintaining voltage stability [104] or decreasing active power losses [105].…”