Demand Shaping to Achieve Steady Electricity Consumption with Load Balancing in a Smart Grid

The purpose of this paper is to study conflicting objectives between the grid operator and consumers in a future smart grid. Traditionally, customers in electricity grids have different demand profiles and it is generally assumed that the grid has to match and satisfy the demand profiles of all its users. However, for system operators and electricity producers, it is usually most desirable, convenient and cost effective to keep electricity production at a constant rate. The temporal variability of electricity demand forces power generators, especially load following and peaking plants to constantly manipulate electricity production away from a steady operating point

Minimizing the impact of EV charging on the electricity distribution network

The main objective of this paper is to design electric vehicle (EV) charging policies which minimize the impact of charging on the electricity distribution network (DN). More precisely, the considered cost function results from a linear combination of two parts: a cost with memory and a memoryless cost. In this paper, the first component is identified to be the transformer ageing while the second one corresponds to distribution Joule losses. First, we formulate the problem as a non-trivial discrete-time optimal control problem with finite time horizon. It is non-trivial because of the presence of saturation constraints and a non-quadratic cost. It turns out that the system state, which is the transformer hot-spot (HS) temperature here, can be expressed as a function of the sequence of control variables; the cost function is then seen to be convex in the control for typical values for the model parameters. The problem of interest thus becomes a standard optimization problem. While the corresponding problem can be solved by using available numerical routines, three distributed charging policies are provided. The motivation is threefold: to decrease the computational complexity; to model the important scenario where the charging profile is chosen by the EV itself; to circumvent the allocation problem which arises with the proposed formulation. Remarkably, the performance loss induced by decentralization is verified to be small through simulations. Numerical results show the importance of the choice of the charging policies. For instance, the gain in terms of transformer lifetime can be very significant when implementing advanced charging policies instead of plug-and-charge policies. The impact of the accuracy of the non-EV demand forecasting is equally assessed.Comment: 6 pages, 3 figures, keywords: electric vehicle charging, electricity distribution network, optimal control, distributed policies, game theor

Customer Engagement Plans for Peak Load Reduction in Residential Smart Grids

In this paper, we propose and study the effectiveness of customer engagement plans that clearly specify the amount of intervention in customer's load settings by the grid operator for peak load reduction. We suggest two different types of plans, including Constant Deviation Plans (CDPs) and Proportional Deviation Plans (PDPs). We define an adjustable reference temperature for both CDPs and PDPs to limit the output temperature of each thermostat load and to control the number of devices eligible to participate in Demand Response Program (DRP). We model thermostat loads as power throttling devices and design algorithms to evaluate the impact of power throttling states and plan parameters on peak load reduction. Based on the simulation results, we recommend PDPs to the customers of a residential community with variable thermostat set point preferences, while CDPs are suitable for customers with similar thermostat set point preferences. If thermostat loads have multiple power throttling states, customer engagement plans with less temperature deviations from thermostat set points are recommended. Contrary to classical ON/OFF control, higher temperature deviations are required to achieve similar amount of peak load reduction. Several other interesting tradeoffs and useful guidelines for designing mutually beneficial incentives for both the grid operator and customers can also be identified

A renewable energy grid daily pricing model for consumer

A Renewable Smart Energy Grid is a global challenge which has to address varied complex issues. In response to this challenge, we present an algorithm for Real-Time Price Suggestion (RTPS) that allows for utilisation within the Smart Grid (SG). Our model achieves a complex optimization of the personal (individual) energy needs of a consumer and minimization of their energy costs. As the SG represents a bidirectional grid the RTPS manages the balance between energy consumer need and a provider to optimize cost savings via a Demand Response (DR) model. The RTPS was designed and validated using real energy network data and has demonstrated that energy consumers can reduce their energy expenditure. Our model integrates the following metrics; Price Suggestion Unit (PSU), Price Control Unit (PCU), Smart Meter (SM) data and along with user appliances, the proposed Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm which varies prices on the basis of a users’ consumption. Thus, we believe that the RTPS algorithm accommodates users’ preferences and non-responsiveness in order to save their energy cost, thereby, safeguarding individual consumer rights and users’ social welfare

Influential Article Review - Development of Open-Loop Coordination Strategies

This paper examines demand. We present insights from a highly influential paper. Here are the highlights from this paper: The activation of flexible loads through demand side management offers opportunities for more efficient power systems operations. Price-based incentives are a straight-forward form for decentral coordination of these flexible loads. However, their applicability has recently been seen more pessimistic as they may induce new load peaks due to herding effects. We revisit these results by characterizing desynchronized posted pricing approaches. Illustrating highly flexible load by means of electric vehicle charging, we show that these desynchronized rates can mitigate the occurrence of extreme load spikes, improve the utilization of renewable generation and in summary create significant system cost savings. Our results show that simple open-loop pricing can almost match the efficiency of closed-loop adaptive pricing in settings with limited system flexibility. We find that the more renewable generation and flexible load are present in the system, the better more complex pricing schemes fare compared to simple ones. This insight may guide regulators and utilities in establishing more effective pricing schemes in retail electricity markets. For our overseas readers, we then present the insights from this paper in Spanish, French, Portuguese, and German

DESIGN AND IMPLEMENT DYNAMIC PROGRAMMING BASED DISCRETE POWER LEVEL SMART HOME SCHEDULING USING FPGA

With the development and capabilities of the Smart Home system, people today are entering an era in which household appliances are no longer just controlled by people, but also operated by a Smart System. This results in a more efficient, convenient, comfortable, and environmentally friendly living environment. A critical part of the Smart Home system is Home Automation, which means that there is a Micro-Controller Unit (MCU) to control all the household appliances and schedule their operating times. This reduces electricity bills by shifting amounts of power consumption from the on-peak hour consumption to the off-peak hour consumption, in terms of different “hour price”. In this paper, we propose an algorithm for scheduling multi-user power consumption and implement it on an FPGA board, using it as the MCU. This algorithm for discrete power level tasks scheduling is based on dynamic programming, which could find a scheduling solution close to the optimal one. We chose FPGA as our system’s controller because FPGA has low complexity, parallel processing capability, a large amount of I/O interface for further development and is programmable on both software and hardware. In conclusion, it costs little time running on FPGA board and the solution obtained is good enough for the consumers

A Distributed Diffusion-Driven Algorithm for Load Balancing in an Electrical Power Grid

In this thesis we propose a distributed algorithm, based on diffusion, to balance loads on an electrical power grid, while maintaining stable operation (system’s ability to maintain bus voltages within preset bounds). This algorithm, called the Diffusion-driven Distributed Load Balancing (DDLB) algorithm, is implemented on the OMNET++ Discrete Event Simulator and the response of the physical grid is simulated on a load flow program, which together simulate a deployment of the DDLB algorithm on the grid. The electrical grid is represented as a graph whose nodes are buses and whose edges are power lines connecting buses. Each node (except the slack bus) has a load (positive if power consumed, negative if power generated) associated with it. The slack bus is a special bus that covers any power surplus or deficit due to a load assignment. A given load assignment, when applied to the grid affects bus voltages and system stability. The problem we address is as follows. Given a preferred load for each node and a load cost (a measure of deviation from this preferred load), the ideal solution is a load assignment with lowest cost that results in a stable system. We measure the performance of our algorithm (DDLB) against the one-shot algorithm, a naive distributed solution in which each node uses its preferred load directly for a load assignment, without any regard for system stability. Through extensive simulations with 1.6 million test cases, we show that the DDLB algorithm vastly outperforms one-shot. Specifically, the one-shot algorithm causes instability in over 57% of the cases tested albeit with zero load cost. For the same cases when applied to the DDLB algorithm only 0.65% were unstable; the average load cost was less than 2%. Our simulations included a study of several scenarios that a grid could be subjected to, including balanced load, overloaded, underloaded grids, local generator failures, and a sparser communication network for the DDLB algorithm; in this context one could view the one-shot algorithm as a distributed algorithm with no communication network. In all these scenarios studied the DDLB algorithm outperforms the one-shot algorithm