Pedram Samadi

Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design

In the future smart grid, both users and power companies can potentially benefit from the economical and environmental advantages of smart pricing methods to more effectively reflect the fluctuations of the wholesale price into the customer side. In addition, smart pricing can be used to seek social benefits and to implement social objectives. To achieve social objectives, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. In this paper, we propose an efficient pricing method to tackle this problem. We assume that each user is equipped with an energy consumption controller (ECC) as part of its smart meter. All smart meters are connected to not only the power grid but also a communication infrastructure. This allows two-way communication among smart meters and the utility company. We analytically model each users preferences and energy consumption patterns in form of a utility function. Based on this model, we propose a Vickrey-Clarke-Groves (VCG) mechanism which aims to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each users electricity bill payment. Finally, we verify some important properties of our proposed VCG mechanism for demand side management such as efficiency, user truthfulness, and nonnegative transfer. Simulation results confirm that the proposed pricing method can benefit both users and utility companies.

Load Scheduling and Power Trading in Systems With High Penetration of Renewable Energy Resources

In this paper, we focus on the problems of load scheduling and power trading in systems with high penetration of renewable energy resources (RERs). We adopt approximate dynamic programming to schedule the operation of different types of appliances including must-run and controllable appliances. We assume that users can sell their excess power generation to other users or to the utility company. Since it is more profitable for users to trade energy with other users locally, users with excess generation compete with each other to sell their respective extra power to their neighbors. A game theoretic approach is adopted to model the interaction between users with excess generation. In our system model, each user aims to obtain a larger share of the market and to maximize its revenue by appropriately selecting its offered price and generation. In addition to yielding a higher revenue, consuming the excess generation locally reduces the reverse power flow, which impacts the stability of the system. Simulation results show that our proposed algorithm reduces the energy expenses of the users. The proposed algorithm also facilitates the utilization of RERs by encouraging users to consume excess generation locally rather than injecting it back into the power grid.

Optimal energy consumption scheduling using mechanism design for the future smart grid

In the future smart grid, both users and power companies can benefit from real-time interactions and pricing methods which can reflect the fluctuations of the wholesale price into the demand side. In addition, smart pricing can be used to seek social benefits and to achieve social objectives. However, the utility company may need to collect various information about users and their energy consumption behavior, which can be challenging. That is, users may not be willing to reveal their local information unless there is an incentive for them to do so. In this paper, we propose an efficient pricing algorithm to tackle this problem. The benefit that each user obtains from each appliance can be modeled in form of a utility function, a concept from microeconomics. We propose a Vickrey-Clarke-Groves (VCG) based mechanism for our problem formulation aiming to maximize the social welfare, i.e., the aggregate utility functions of all users minus the total energy cost. Our design requires that each user provides some information about its energy demand. In return, the energy provider will determine each users payment for electricity. The payment of each user is structured in such a way that it is in each users self interest to reveal its local information truthfully. Finally, we present simulation results to show that both the energy provider and the individual users can benefit from the proposed pricing algorithm.

Residential Demand Side Management Under High Penetration of Rooftop Photovoltaic Units

In a residential area where many households have installed rooftop photovoltaic (PV) units, there is a reverse power flow from the households to the substation when the power generation from PV units is larger than the aggregate load of the households. This reverse power flow causes the voltage rise problem. In this paper, we study the use of demand side management to mitigate the voltage rise problem. We propose an autonomous energy consumption scheduling algorithm, which schedules the operation of deferrable loads to jointly shave the peak load and reduce the reverse power flow. The proposed algorithm shifts the operation of deferrable loads from peak consumption hours to hours with high-power generation from the PV units. We use stochastic programming to formulate an energy consumption scheduling problem, which takes into account the uncertainty related to the amount of power generation from PV units. The formulated cost function comprises a monetary cost for energy consumption, the revenue from energy export, and an external cost for the voltage rise. Numerical results show that our proposed algorithm can mitigate the voltage rise problem in areas with high penetration of PV units and reduce the peak-to-average ratio of the aggregate load.

Power dispatch and load control with generation uncertainty

In this paper, we focus on the problem of joint load scheduling and generation management to better match supply and demand. We formulate an optimization problem to jointly minimize the generation cost and discomfort cost of the users subject to the voltage and power balance equations for the equivalent circuit of the power system. The optimal power flow (OPF) equations are solved using semidefinite programming (SDP) relaxation technique. In our system model, we assume that users can exploit renewable energy resources (RERs). RERs are random in nature and may cause voltage variations in different nodes of the system. To minimize the risk of having high voltage values, a barrier term is added to the objective function. This term is calculated based on the concept of conditional value-at-risk (CVaR). Simulation results show that compared to the case where there is no control over the load, our proposed algorithm reduces the generation cost by better matching the generation and demand. Moreover, the proposed algorithm reduces the voltage variations at different nodes of the system.

Tackling the photovoltaic integration challenge in the distribution network with deferrable load

In recent years, there is an increasing deployment of photovoltaic (PV) units and energy storage systems (ESSs) in households. When the energy generated by PV units is greater than the aggregate load of households and the capacity of ESS, there will be a reverse energy flow from households to the substation. When the reverse energy flow exceeds a certain threshold, it will cause a voltage rise problem, which is a challenge for PV units to be effectively integrated with the distribution network. In this paper, we propose an energy consumption scheduling algorithm, which shifts the deferrable load (e.g., washing machines, dryers) from peak hours (e.g., 7 pm - 10 pm) to high solar radiation hours (e.g., 10 am - 2 pm) in order to jointly shave the peak load and reduce the reverse energy flow. We formulate the energy consumption scheduling problem as a stochastic optimization problem to capture the uncertainty of the amount of PV output power. The objective of our algorithm is to minimize the electricity bill for the household users which have PV units and ESS installed. We use inclining block rate (IBR) pricing and time of use (TOU) pricing to encourage users to shift their load. Numerical results show that our proposed algorithm can avoid the voltage rise problem and reduce the peak-to-average ratio (PAR) in the aggregate load.

Smart Grid Communications and Networking: Demand-side management for smart grid: opportunities and challenges

Introduction Demand-side management (DSM) is one of the key components of the future smart grid to enable more efficient and reliable grid operation [1]. To achieve a high level of reliability and robustness in power systems, the grid is usually designed for peak demand rather than for average demand. This usually results in an under-utilized system. To remedy this problem, different programs have been proposed to shape the daily energy consumption pattern of the users in order to reduce the peak-to-average ratio in load demand and use the available generating capacity more efficiently, avoiding the installation of new generation and transmission infrastructures. However, the increasing expectations of the customers both in quantity and quality [2], emerging new types of demand such as plug-in hybrid electric vehicles (PHEVs), which can potentially double the average household energy consumption [3], the limited energy resources, and the lengthy and expensive process of exploiting new resources give rise to the need for developing some more advanced methods for DSM. Since electricity cannot be stored economically, wholesale prices (i.e., prices set by competing generators to regional electricity retailers) vary drastically between the low-demand times of day and the high-demand periods. However, these changes are usually hidden from retail users. That is, end users are usually charged with some average price. To alleviate this problem, various time-differentiated pricing methods have been proposed in the literature. Some examples include day-ahead pricing, time-of-use pricing, critical-peak-load pricing, and adaptive pricing [4–7]. By equipping users with two-way communication capabilities in smart grid systems and by adopting real-time pricing (RTP) methods, it is possible to reflect the fluctuations of wholesale prices to retail prices.