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... Generalized approximation algorithms for DER scheduling have only been developed recently. Works such as [24][25][26] developed approximation algorithms assuming the DERs output only real value. While true for traditional inverters, modern smart inverters support injection of both real and reactive values from the DERs. ...

Rapid penetration of renewable energy based Distributed Energy Resources (DER) has the potential to exacerbate the challenges inherent in grid frequency and voltage regulation. However, their real time controllability can be leveraged to not only mitigate such challenges, but improve the economics and quality of grid operations. A plethora of DER scheduling algorithms have been developed for fast and efficient scheduling of these resources. These algorithms schedule the complex - real and reactive power injected into (or drawn from) the grid by the DERs under various cost objective functions and grid constraints. However, a vast majority of such algorithms assume the feasible range of power injections to be continuous. In reality, the control options available for several DERs form a discrete space. This implies that the feasible range of power injections also forms a discrete space. This makes DER scheduling an NP-hard problem. Integer/Mixed Integer program based algorithms, which guarantee optimality in the objective value, have been developed. But these are prohibitively expensive in terms of computational time requirements. On the other hand, fast heuristic based solutions have also been developed. But as they do not provide any guarantee on the optimality of the objective, or on satisfying the constraints, they are unreliable for grid operations. Recent works have developed approximation algorithms which offer the best of both the approaches above: (near) optimality and low computational complexity, both of which are governed by a user defined accuracy parameter ∈. In this work, we significantly improve upon such existing works by developing an approximation algorithm which schedules DERs with complex injections (as opposed to only real injection) and whose runtime is polynomial (as opposed to exponential) in 1/∈. We provide theoretical analysis and support them with experimental results to validate the guarantees provided by our approximation algorithm.

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.

Continued advances in technology have led to falling costs and a dramatic increase in the aggregate amount of solar capacity installed across the world. A drawback on increased solar penetration is the potential for supply-demand mismatches in the grid due to the intermittent nature of solar generation. While energy storage can be used to mask such problems, we argue that there is also a need to explicitly control the rate of solar generation of each solar array in order to achieve high penetration while also handling supply-demand mismatches. To address this issue, we present the notion of smart solar arrays that can actively modulate their solar output based on the notion proportional fairness. We present a decentralized algorithm based on Lagrangian optimization that enables each smart solar array to make local decisions on its fair share of solar power it can inject into the grid, and then present a sense-broadcast-respond protocol to implement our decentralized algorithm into smart solar arrays. Our evaluation on a city-scale dataset shows that our approach enables 2.6x more solar penetration, while causing smart arrays to reduce their output by as little as 12.4%. By employing an adaptive gradient approach, our decentralized algorithm has 3 to 30x faster convergence. Finally, we implement our distributed algorithm on a Raspberry Pi-class processor to demonstrate its feasibility on grid-tied solar inverters with limited processing capability.

The electric grid was not designed to support the large-scale penetration of intermittent solar generation. As a result, current policies place hard caps on the solar capacity that may connect to the grid. Unfortunately, users are increasingly hitting these caps, which is restricting the natural growth of solar power. To address the problem, we propose Software-defined Solar-powered (SDS) systems that dynamically regulate the amount of solar power that flows into the grid. To enable SDS systems, this paper introduces fundamental mechanisms for programmatically controlling the size of solar flows, including mechanisms to both enforce an absolute limit on solar output and a new class of Weighted Power Point Tracking (WPPT) algorithms that enforce a relative limit on solar output as a fraction of its maximum power point (MPP). We implement an SDS prototype, called SunShade, and evaluate tradeoffs in the accuracy and fidelity of these mechanisms to enforce limits on solar flows. For example, we quantify the effects of variable conditions, such as clouds, passersby, and other shading, on the fidelity of a search-based WPPT algorithm, which must periodically deviate from its cap to discover changes in the MPP that affect the cap's accuracy.

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.

A framework for the power management in a smart campus environment is proposed, which enables the integration of renewable local energy sources, storage banks and controllable loads, and supports Demand Response with the electricity grid operators. We describe the system components, including an Energy Management System for the optimal scheduling of power usage, a telecommunication infrastructure for data exchange , and power production/consumption forecast algorithms. We also analyzes relevant use cases and propose quality metrics for the performance validation of the framework.

Transition towards smart distribution networks with high penetration of photovoltaics (PV) will involve incidental generation curtailment as an alternative to grid reinforcements. Micro-inverters are taking over popularity of string inverters in residential and some commercial areas mainly due to increased
energy harvest. This paper demonstrates how micro-inverters with a modified overvoltage protection scheme could provide a reliable curtailment solution and accommodate additional PV capacity. Two wide-area curtailment schemes were proposed for a typical Dutch residential feeder with densely clustered PV.
Firstly, a single worst-case scenario was used to demonstrate the capabilities of the proposed curtailment schemes: the distribution network operators can optimize between various priorities such as total feeder output, economic equality between connected parties, voltage levels, voltage unbalance and curtailment
execution time. Secondly, a yearly comparison was made against conventional overvoltage protection and the results show 62-100% reduction in overvoltage losses.

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in response to a request sent from the utility whenever it anticipates a peak in demand. To successfully plan and implement demand response, the utility requires reliable estimate of reduced consumption during DR. This also helps in optimal selection of consumers and curtailment strategies during DR. While much work has been done on predicting normal consumption, reduced consumption prediction is an open problem that is under-studied. In this paper, we introduce and formalize the problem of reduced consumption prediction, and discuss the challenges associated with it. We also describe computational methods that use historical DR data as well as pre-DR conditions to make such predictions. Our experiments are conducted in the real-world setting of a university campus micro-grid, and our preliminary results set the foundation for more detailed modeling.

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 minimizing the load demand while not dealing with the uncertainty induced by customer intervention which hinder sustainability of the reduced load. In this paper we describe a smart selection algorithm that solves the problem of scheduling DR events in a broad spectrum of customers observed in common Smart Grid Infrastructures. We deal both with the problem of real-time operation and sustainability of the reduced load while factoring customer comfort levels. Real Data were used from the USC campus micro grid in our experiments. On the overall achievable reduction the results produced a maximum average approximation error ≈ 0.7%. Sustainability of the targeted load was achieved with maximum average error of less than 3%. It is also shown that our solution fulfils the requirements for Dynamic Demand Response providing a solution in a reasonable amount of time.

The drive towards more sustainable power supply systems has enabled significant growth of renewable generation. This in turn has pushed the rollout of demand response (DR) programs to address a larger population of consumers. Utilities are interested in enrolling small and medium sized customers that can provide demand curtailment during periods of shortfall in renewable production. It then becomes important to be able to target the right customers among the large population, since each enrollment has a cost. The availability of high resolution information about each consumers demand consumption can significantly change how such targeting is done. This paper develops a methodology for large scale targeting that combines data analytics and a scalable selection procedure. We propose an efficient customer selection method via stochastic knapsack problem solving and a simple response modeling in one example DR program. To cope with computation issues coming from the large size of data set, we design a novel approximate algorithm.

Recently, the smart grid and microgrid based on information technology (IT) and their applications have being studied worldwide because of its high-efficiency energy consumption and variety of energy. Many universities are studying a green campus to operate the energy system efficiently based on the microgrid. Especially, it is important to meet the balance between energy supply and load with minimal cost in the green campus operation. Especially, the demand response (DR) is an important function of optimal operation on the smart grid environment. In this paper, a mathematical model for optimal operation based on DR of the energy system of the green campus is proposed. Through the simulation, its feasibility is tested and analyzed.

This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic optimization and robust optimization approaches. The proposed real-time price-based DR management application can be imbedded into smart meters and automatically executed on-line for determining the optimal operation of residential appliances within 5-minute time slots while considering uncertainties in real-time electricity prices. Operation tasks of residential appliances are categorized into deferrable/non-deferrable and interruptible/non-interruptible ones based on appliances' DR preferences as well as their distinct spatial and temporal operation characteristics. The stochastic optimization adopts the scenario-based approach via Monte Carlo (MC) simulation for minimizing the expected electricity payment for the entire day, while controlling the financial risks associated with real-time electricity price uncertainties via the expected downside risks formulation. Price uncertainty intervals are considered in the robust optimization for minimizing the worst-case electricity payment while flexibly adjusting the solution robustness. Both approaches are formulated as mixed-integer linear programming (MILP) problems and solved by state-of-the-art MILP solvers. The numerical results show attributes of the two approaches for solving the real-time optimal DR management problem for residential appliances.

The concept of integration of distributed energy resources for formation of microgrid will be most significant in near future. The latest research and development in the field of microgrid as a promising power system through a comprehensive literature review is presented in this paper. It shows a broad overview on the worldwide research trend on microgrid which is most significant topic at present. This literature survey reveals that integration of distributed energy resources, operation, control, power quality issues and stability of microgrid system should be explored to implement microgrid successfully in real power scenario.

This paper presents a mixed integer linear programming formulation for load-side control of electrical energy demand. The formulation utilizes demand prediction to determine if control actions are necessary, and it schedules both shedding and restoration times based on an optimization model that minimizes the net cost of load shedding. Operational constraints are satisfied through the use of minimum/maximum uptimes/downtimes, which depend upon the current state of the system. The algorithm is evaluated using a simulation model of an underground coal mining operation where, (i) its performance is compared with a traditional static, priority-based, load-shedding schedule, and, (ii) its potential is established for producing net savings through demand control.

This paper is concerned with scheduling of demand response among different residences and a utility company. The utility company has a cost function representing the cost of providing energy to end-users, and this cost can be varying across the scheduling horizon. Each end-user has a “must-run” load, and two types of adjustable loads. The first type must consume a specified total amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. The second type of load has adjustable power consumption without a total energy requirement, but operation of the load at reduced power results in dissatisfaction of the end-user. The problem amounts to minimizing the total cost electricity plus the total user dissatisfaction (social welfare), subject to the individual load consumption constraints. The problem is convex and can be solved by a distributed subgradient method. The utility company and the end-users exchange Lagrange multipliers-interpreted as pricing signals-and hourly consumption data through the Advanced Metering Infrastructure, in order to converge to the optimal amount of electricity production and the optimal power consumption schedule.

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Implementation of learning-based dynamic demand response on a campus micro-grid

- S R Kuppannagari
- R Kannan
- C Chelmis
- V K Prasanna