Real-time Price Signals Research Articles

Robust Optimal Operation of AC/DC Hybrid Microgrids Under Market Price Uncertainties

Energy management system (EMS) is responsible for the optimal operation of microgrids. EMS adjusts its operational schedule for near future by using the available information. Market price signals are generally used for the operation of microgrids, which are obtained by using estimation/ forecasting methods. However, it is difficult to precisely predict the market prices due to the involvement of various complex factors like weather, policy, demand, errors in forecasting methods, and fuel cost. Therefore, in this paper, the uncertainties associated with the real-time market price signals (buying and selling) are realized via a robust optimization method. In addition to market price signals, uncertainties associated with renewable power sources and forecasted load values are also considered. Initially, a deterministic model is formulated for an ac/dc hybrid microgrid. Then a min–max robust counterpart is formulated by considering the worst-case uncertainties. Finally, an equivalent mixed integer problem is formulated by using linear duality and other optimality conditions. The developed model can provide feasible solutions for all the scenarios if the uncertainties fluctuate within the specified bounds. The effect of market price uncertainties on internal power transfer and external power trading, operation cost, the state-of-charge of energy storage elements, and unit commitment of dispatchable generators is analyzed. Taguchi’s orthogonal array (OA) method is used to find the worst-case scenario within the specified uncertainty bounds. Then, Monte Carlo method is used to generate various scenarios within the uncertainty bounds to evaluate the robustness of the selected scenario via Taguchi’s OA method. Finally, a violation index is formulated to evaluate the robustness of the proposed approach against the deterministic model. Simulations results have validated the robustness of the proposed optimization strategy.

Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes

The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM). Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR) with minimum user waiting time. There are many studies on Home Energy Management (HEM) for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction) at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA), the recently developed Cuckoo Search Optimization Algorithm (CSOA) and the Crow Search Algorithm (CSA), which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS) is also taken into account for more efficient operation of the Home Energy Management System (HEMS). Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments), Real-Time Pricing (RTP) and Critical Peak Pricing (CPP) signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and PAR minimization. Moreover, there exists a tradeoff between electricity cost and user waiting.

Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Multi-agent-based sharing power economy for a smart community

Summary In this paper, a multi-agent-based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small-scale battery ESSs (each owned by community users), which are connected together to form a unified-ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified-ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified-ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi-agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on-peak, off-peak, and mid-level (mid-peak) prices of a real-time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.

Indirect Load Shaping for CHP Systems Through Real-Time Price Signals

Direct or centralized loading shaping in smart grid has been heavily investigated. However, it is usually not clear how the users are compensated by providing load shaping services. In this paper, we will discuss indirect load shaping in a distributed manner. On one hand, we aim to reduce the users’ energy cost by investigating how to fully utilize the battery pack and the water tank for the combined heat and power (CHP) systems. We first formulate the queueing models for the CHP systems and then propose an algorithm based on the Lyapunov optimization technique, which does not need any statistical information about the system dynamics. The optimal control actions can be obtained by solving a nonconvex optimization problem. We then discuss when it can be converted into a convex optimization problem. Since the CHP battery pack queue and water tank queue are correlated, the capacity relationship between them is further explored considering different queue weights. On the other hand, based on the users’ reaction model, we propose an algorithm with a time complexity of ${O}$ (log ${n}$ ) to determine the real-time price for the power company to effectively coordinate all the CHP systems and provide distributed load shaping services.

Time-based electricity pricing for large-volume customers: A comparison of two buildings under tariff alternatives

This study investigates a variety of pricing policies for commercial and industrial customer sectors. Two building examples were used to analyze energy usage by buildings under the uniform-rate and time-based pricing schedules of three electricity utilities. Critical-peak and real-time price signals encourage building managers to implement the greatest number of measures to reduce their electricity costs. Consumption-shifting and curtailment measures that take into account time-based electricity prices do not appear to reduce energy usage and costs as much as regular energy-efficiency measures. Nonetheless, expanding the focus of energy management to include measures based on time-based pricing can help reduce electricity costs for commercial and industrial buildings.

Integrity Attacks on Real-Time Pricing in Electric Power Grids

Modern information and communication technologies used by electric power grids are subject to cyber-security threats. This article studies the impact of integrity attacks on real-time pricing (RTP), an emerging feature of advanced power grids that can improve system efficiency. Recent studies have shown that RTP creates a closed loop formed by the mutually dependent real-time price signals and price-taking demand. Such a closed loop can be exploited by an adversary whose objective is to destabilize the pricing system. Specifically, small malicious modifications to the price signals can be iteratively amplified by the closed loop, causing highly volatile prices, fluctuating power demand, and increased system operating cost. This article adopts a control-theoretic approach to deriving the fundamental conditions of RTP stability under basic demand, supply, and RTP models that characterize the essential behaviors of consumers, suppliers, and system operators, as well as two broad classes of integrity attacks, namely, the scaling and delay attacks. We show that, under an approximated linear time-invariant formulation, the RTP system is at risk of being destabilized only if the adversary can compromise the price signals advertised to consumers, by either reducing their values in the scaling attack or providing old prices to over half of all consumers in the delay attack. The results provide useful guidelines for system operators to analyze the impact of various attack parameters on system stability so that they may take adequate measures to secure RTP systems.

Decentralized Vehicle-to-Grid Design for Frequency Regulation within Price-based Operation

The utilization of electric vehicles has been suggested to support the frequency regulation of power system. Assuming that an intermediate aggregator exists, this study suggests a decentralized vehicle-to-grid operation scheme in which each vehicle-to-grid aggregator can behave independently of the power system operator. To implement this type of decentralized operation, this study adopts a price-based operation that has been proposed by many researches as an alternative operation scheme for the power system. In this environment, each vehicle-to-grid aggregator can determine its participation in vehicle-to-grid service in consideration of its residual energy of aggregated system and real-time market price. Consequently, the main purpose of this study is to verify whether or not the vehicle-to-grid power can effectively support the current frequency regulation function within the price-based operation scheme. Specifically, a frequency regulation method is proposed based on the real-time price signal, and a feedback controller for battery management is designed for decentralized vehicle-to-grid operation.

Sensitivity-Based Pricing and Optimal Storage Utilization in Distribution Systems

In the deregulated power system, locational marginal prices (LMPs) are used in transmission engineering predominantly as near real-time pricing signals. This work extends this concept to distribution engineering by formulating a distribution LMP signal based on power flow sensitivities in a distribution system. A Jacobian-based sensitivity analysis has been developed for application in the distribution pricing method. Increasing deployment of distributed energy sources and storage devices is being seen at the distribution level and this trend is expected to continue. The optimal operation of the distributed infrastructure has been formulated as a multiobjective optimization problem and a solution algorithm has been developed that is based on the normal boundary intersection method. A parallel processing environment has been used to replicate the distributed nature of controls at many points in the distribution system. The Roy Billinton Test System has been used to demonstrate the applications.

Electric grid balancing through lowcost workload migration

Energy production must continuously match demand on the electric grid. A deficiency can lead to service disruptions, and a surplus can place tremendous stress on grid components, potentially causing major blackouts. To manage this balance, grid operators must increase or lower power generation, with only a few minutes to react. The grid balancing problem has also impeded the pace of integrating bountiful renewable resources (e.g., wind), whose generation is intermittent. An emerging plan to mitigate this problem is demand response, i.e., for grid operators to alter the electricity usage behavior of the masses through real-time price signals. But due to prohibitively high infrastructure costs and societal-scale adoption, tangible demand response mechanisms have so far been elusive. We believe that altering the usage patterns of a multitude of data centers can be a tangible, albeit initial, step towards affecting demand response. Growing in both density and size, today's data center designs are shaped by the increasing awareness of energy costs and carbon footprint. We posit that shifting computational workloads (and thus, demand) across geographic regions to match electricity supply may help balance the grid. In this paper we will first present a real grid balancing problem experienced in the Pacfic Northwest. We then propose a symbiotic relationship between data centers and grid operators by showing that mutual cost benefits can be accessible. Finally, we argue for a low cost workload migration mechanism, and pose overarching challenges in designing this framework.

Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid

The integration of renewable energy systems (RESs) in smart grids (SGs) is a challenging task, mainly due to the intermittent and unpredictable nature of the sources, typically wind or sun. Another issue concerns the way to support the consumers' participation in the electricity market aiming at minimizing the costs of the global energy consumption. This paper proposes an energy management system (EMS) aiming at optimizing the SG's operation. The EMS behaves as a sort of aggregator of distributed energy resources allowing the SG to participate in the open market. By integrating demand side management (DSM) and active management schemes (AMS), it allows a better exploitation of renewable energy sources and a reduction of the customers' energy consumption costs with both economic and environmental benefits. It can also improve the grid resilience and flexibility through the active participation of distribution system operators (DSOs) and electricity supply/demand that, according to their preferences and costs, respond to real-time price signals using market processes. The efficiency of the proposed EMS is verified on a 23-bus 11-kV distribution network.

A Reliable Energy Information System for Promoting Voluntary Energy Conservation Benefits

This paper presents the design and development of a reliable PLC/RF ENergy Information System (ENIS) for promoting voluntary energy conservation benefits. With the help of monitoring and browsing energy information immediately, the price signal and visible energy saving profile can provide incentives for customers to migrate peak demand elastically and economically. Two real-world applications of the ENIS are then reported. First, shedding peak load and diminishing energy consumption at NCTU University are explored by managing air conditioners through debit cards and energy meters (EMs). Second, in an aggregate community, the real-time pricing signals can motivate end users to shift peak demand by means of manual or automatic load controls. These examples illustrate that EMs and energy information displays with enhanced reliability are highly applicable for voluntary energy conservation in densely populated areas.