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Regulating the power consumption to avoid peaks in demand is a known method. Demand Response is used as tool by utility providers to minimize costs and avoid network overload during peaks in demand. Although it has been used extensively there is a shortage of solutions dealing with real-time scheduling of DR events. Past attempts focus on minimizin...

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**Context 1**

... this case(AVG) none of the methods we developed can match the performance of the greedy technique. In (Fig.5) we chose to present the results for the case of MAX since they are similar to that of MAVG although a little bit worst. ...

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## Citations

... The first is to forgo accuracy guarantees in favor of performance. Techniques such as [39,27,50,40] develop fast algorithms that can have arbitrarily large errors in the objective function (utility maximization, cost minimization, etc.). Authors in [39] develop a genetic-algorithm-based heuristic, while [50] presents a heuristic based on change making. ...

... Techniques such as [39,27,50,40] develop fast algorithms that can have arbitrarily large errors in the objective function (utility maximization, cost minimization, etc.). Authors in [39] develop a genetic-algorithm-based heuristic, while [50] presents a heuristic based on change making. The algorithm developed in [40] uses Linear Programming, whose solutions need to be rounded to integral values and can have large errors (unbounded integrality gap). ...

... We also compare our algorithm against demand curtailment selection techniques such as those developed in [50] and [17]. We observed that these techniques typically incur errors (constraint violations) of around 5% to 20% and in the worst case can go as high as 95%. ...

Mitigating supply-demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as demand response have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for smart grids with high PV penetration. These grids sometimes exhibit supply surplus, causing overvoltages. Currently, these are mitigated using voltage manipulation techniques such as Volt-Var Optimizations, which are computationally expensive, thereby increasing the complexity of grid operations. Taking advantage of recent technological developments that enable rapid selective connection of PV modules of an installation to the grid, we develop a unified net-load balancing framework that performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and we develop bounded approximation algorithms. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation, while ensuring that practical constraints such as fairness, network capacity limits, and so forth are satisfied. We also develop an online algorithm that performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions that are close to optimal in a small amount of time.

... The notion of achieving sustainable DR over a peak period divided into subintervals was proposed in [20] using a change making heuristic to evenly distribute curtailment over intervals. However, a detailed analysis (omitted due to space constraints) shows that it achieves consistency between intervals without reference to the target leading to unbounded errors which is also demonstrated by our experimental results. ...

... Since the ILPs are solved exactly, the respective errors are optimal. We compare the optimal minimal errors with the actual errors achieved by the stateof-the-art heuristic [20]. ...

Demand Response (DR) is a widely used technique to minimize the peak to average consumption ratio during high demand periods. We consider the DR problem of achieving a given curtailment target for a set of consumers equipped with a set of discrete curtailment strategies over a given duration. An effective DR scheduling algorithm should minimize the curtailment error - the difference between the targeted and achieved curtailment values - to minimize costs to the utility provider and maintain system reliability. The availability of smart meters with fine-grained customer control capability can be leveraged to offer customers a dynamic range of curtailment strategies that are feasible for small durations within the overall DR event. Both the availability and achievable curtailment values of these strategies can vary dynamically through the DR event and thus the problem of achieving a target curtailment over the entire DR interval can be modeled as a dynamic strategy selection problem over multiple discrete sub-intervals. We argue that DR curtailment error minimizing algorithms should not be oblivious to customer curtailment behavior during sub-intervals as (expensive) demand peaks can be concentrated in a few sub-intervals while consumption is heavily curtailed during others in order to achieve the given target, which makes such solutions expensive for the utility. Thus in this paper, we formally develop the notion of Sustainable DR (SDR) as a solution that attempts to distribute the curtailment evenly across sub-intervals in the DR event. We formulate the SDR problem as an Integer Linear Program and provide a very fast $\sqrt{2}$-factor approximation algorithm. We then propose a Polynomial Time Approximation Scheme (PTAS) for approximating the SDR curtailment error to within an arbitrarily small factor of the optimal. We then develop a novel ILP formulation that solves the SDR problem while explicitly accounting for customer strategy switching overhead as a constraint. We perform experiments using real data acquired from the University of Southern California’s smart grid and show that our sustainable DR model achieves results with a very low absolute error of 0.001-0.05 kWh range.

... In this case, the utility will monitor the demand of each home and take actions based on predicted consumption. By employing customer selection algorithms [10], [11], utilities can target specific areas without impacting the same subset of customers repeatedly, hence reducing the impact on their comfort. However, despite DR being an effective means of curtailment energy consumption, utilities may face the reluctance of customers to yield control of their homes. ...

As smart homes and smart grids become ubiquitous their interactions will become crucial for optimizing energy consumption at large scale at residential level. Scalable solutions will be required to enable fast and reliable control during demand response. While management solutions have been proposed they do not focus on the scalability issues of the processing system. Handling continuous and variable Big Data streams can easily saturate existing systems. In this paper we propose a scalable cloud based architecture and prototype system for handling smart home data ﬂows. The system can support near real time decisions for 10,000 customers each having 10 sensors with only 35 commodity machines running free cloud software. The platform is automated and can be used to directly control the customers’ smart home or to send recommendations. Some initial experiments are performed to show the beneﬁts of smart recommendations.

Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response (DR) have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for Smart Grids with high PV penetration. These grids can sometimes exhibit supply surplus causing over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are complex and computationally expensive. This increases the complexity of net-load balancing systems used by the grid operator and limits their scalability. Recently new technologies have been developed that enable the rapid and selective connection of PV modules of an installation to the grid. Taking advantage of these advancements, we develop a unified optimal net-load balancing framework which performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and develop bounded approximation algorithms for minimizing the curtailment cost. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation. We also incorporate the notion of fairness to ensure that curtailment is evenly distributed among all the nodes. Finally, we develop an online algorithm which performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions which are close to optimal in a small amount of time.

Maintaining the balance between electricity supply and demand is one of the major concerns of utility operators. With the increasing contribution of renewable energy sources in the typical supply portfolio of an energy provider, volatility in supply is increasing while the control is decreasing. Real time pricing based on aggregate demand, unfortunately cannot control the non-linear price sensitivity of deferrable/flexible loads and leads to other peaks [4, 5] due to overly homogenous consumption response. In this paper, we present a day-ahead group-based real-time pricing mechanism for optimal demand shaping. We use agent-based simulations to model the system-wide consequences of deploying different pricing mechanisms and design a heuristic search mechanism in the strategy space to efficiently arrive at an optimal strategy. We prescribe a pricing mechanism for each groups of consumers, such that even though consumption synchrony within each group gives rise to local peaks, these happen at different time slots, which when aggregated result in a flattened macro demand response. Simulation results show that our group-based pricing strategy out-performs traditional real-time pricing, and results in a fairly flat peak-to-average ratio.