Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

Distributed smart charging of electric vehicles for balancing wind energy

To meet worldwide goals of reducing CO2 footprint, electricity production increasingly is stemming from so-called renewable sources. To cater for their volatile behavior, so-called demand response algorithms are required. In this paper, we focus particularly on how charging electrical vehicles (EV) can be coordinated to maximize green energy consumption. We present a distributed algorithm that minimizes imbalance costs, and the disutility experienced by consumers. Our approach is very much practical, as it respects privacy, while still obtaining near-optimal solutions, by limiting the information exchanged: i.e. consumers do not share their preferences, deadlines, etc. Coordination is achieved through the exchange of virtual prices associated with energy consumption at certain times. We evaluate our approach in a case study comprising 100 electric vehicles over the course of 4 weeks, where renewable energy is supplied by a small scale wind turbine. Simulation results show that 68% of energy demand can be supplied by wind energy using our distributed algorithm, compared to 73% in a theoretical optimum scenario, and only 40% in an uncoordinated business-as-usual (BAU) scenario. Also, the increased usage of renewable energy sources, i.e. wind power, results in a 45% reduction of CO2 emissions, using our distributed algorithm

Modeling Storage and Demand Management in Electricity Distribution Grids

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for `smart` charging and slightly improve the case for central storage devices.Storage, demand management, stochastic optimization, Benders Decomposition

A Novel Hybrid Framework for Co-Optimization of Power and Natural Gas Networks Integrated With Emerging Technologies

In a power system with high penetration of renewable power sources, gas-fired units can be considered as a back-up option to improve the balance between generation and consumption in short-term scheduling. Therefore, closer coordination between power and natural gas systems is anticipated. This article presents a novel hybrid information gap decision theory (IGDT)-stochastic cooptimization problem for integrating electricity and natural gas networks to minimize total operation cost with the penetration of wind energy. The proposed model considers not only the uncertainties regarding electrical load demand and wind power output, but also the uncertainties of gas load demands for the residential consumers. The uncertainties of electric load and wind power are handled through a scenario-based approach, and residential gas load uncertainty is handled via IGDT approach with no need for the probability density function. The introduced hybrid model enables the system operator to consider the advantages of both approaches simultaneously. The impact of gas load uncertainty associated with the residential consumers is more significant on the power dispatch of gas-fired plants and power system operation cost since residential gas load demands are prior than gas load demands of gas-fired units. The proposed framework is a bilevel problem that can be reduced to a one-level problem. Also, it can be solved by the implementation of a simple concept without the need for Karush–Kuhn–Tucker conditions. Moreover, emerging flexible energy sources such as the power to gas technology and demand response program are considered in the proposed model for increasing the wind power dispatch, decreasing the total operation cost of the integrated network as well as reducing the effect of system uncertainties on the total operating cost. Numerical results indicate the applicability and effectiveness of the proposed model under different working conditions