Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by a Load Serving Entity

Motivated by the growing importance of demand response in modern power system's operations, we propose an architecture and supporting algorithms for privacy preserving thermal inertial load management as a service provided by the load serving entity (LSE). We focus on an LSE managing a population of its customers' air conditioners, and propose a contractual model where the LSE guarantees quality of service to each customer in terms of keeping their indoor temperature trajectories within respective bands around the desired individual comfort temperatures. We show how the LSE can price the contracts differentiated by the flexibility embodied by the width of the specified bands. We address architectural questions of (i) how the LSE can strategize its energy procurement based on price and ambient temperature forecasts, (ii) how an LSE can close the real time control loop at the aggregate level while providing individual comfort guarantees to loads, without ever measuring the states of an air conditioner for privacy reasons. Control algorithms to enable our proposed architecture are given, and their efficacy is demonstrated on real data.

In Japan the electricity market will open on April 1, 2004. Electric utility, Power Producer and Supplier (PPS), and Load Service Entity (LSE) will join the electricity market. LSEs purchase electricity based on the Market Clearing Price (:MCP) from the electricity market. LSEs supply electricity to the customers that contracted with the LSEs on a certain electricity price, and one to the customers that introduced Energy Storage System (:ES) on a time-of-use pricing. It is difficult for LSEs to estimate whether they have any incentive to promote customers to introduce ES or not. This paper evaluates the reduction of LSEs' purchasing cost from the electricity market and other LSEs' purchasing cost by introducing ES to customers. It is clarified which kind of customers has the effect of decreasing LSEs' purchasing cost and how much MCP of the whole power system the demand-side energy storage systems change. Through numerical examples, this paper evaluates the possibility of giving the cost merit to both customers with energy storage systems and LSE by using real data for a year's worth of MCP. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 158(1): 22–35, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20447

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Modern power system is a typical cyber-physical system (CPS). The stable operation of the modern power system relies increasingly on the well-functioning cyber system. Therefore, it is important to include the possible effects of the cyber system as we assess power system stability. Since our main purpose of considering the cyber system is to study its possible effect on power system operation, and cyber system mainly affect the physical system through affecting the information flow, we mainly focus on the information flow as we model the cyber system. In the proposed model, the details in cyber system operation is not considered to reduce redundant calculation burden. Instead, cyber elements and cyber contingencies are classified and modeled based on their effect on the information flow, reflecting their possible effect on the operation of the power system. We can simulate the CPS by integrating the proposed cyber system model with the power system model and conducting mixed simulation. Case study is carried out in the AVC system in the IEEE-14 bus power system. The result shows the proposed cyber system model and CPS simulation method can be used to analysis the possible effect of cyber contingencies on the power system.

This paper focuses on a two-stage framework for economic optimization to maximize the profits of electric-vehicle (EV) microgrid. In the first stage, an economic optimization problem at the day-ahead time scale is solved to determine the power purchased from load serving entities (LSE), and make an optimal price decision (parking fee and charging fee) while considering with the EVs uncertainties. In the second stage, a real-time model predictive control strategy is proposed to meet the EVs requirement and minimize the operation cost. Through two-stage scheduling, EV microgrid can guarantee long-term safe and efficient operation, while ensuring maximum benefits. The simulation results show that the proposed method in this paper can provide reliable power supply to the EVs, increase the EV microgrid revenue and ensure the safe operation of the EV microgrid system.

The application of unified power flow controller (UPFC) has become an inevitable trend to deal with challenges faced by modern power system such as optimization of power supply and system operation. However, review researches on UPFC are less while the researches on specific technologies mostly focus on theoretical fields, which are difficult to guide actual engineering. To this end, this paper discusses several key issues of UPFC in its actual application. First of all, technical process of UPFC in actual application was proposed and its six key issues were pointed out. Then, for three issues in the process of “scheme-obtaining of UPFC application”, the principles of necessity to configure UPFC, the method of determining UPFC location and capacity, the method of determining UPFC control strategy are discussed. Finally, for three issues in the process of “influence analysis and optimization for UPFC”, simulation calculation method of power system with UPFC, influences of UPFC on power system operation, changes strategy of operating parameter for UPFC are discussed. The work done in this article aims to provide a reference for the actual application of UPFC.

This chapter presents the formulation and critical assessment of a novel type of demand response (DR) program targeting retail customers (such as small/medium size commercial, industrial, and residential customers) who are equipped with smart meters yet still face a flat rate. Enabled by pervasive mobile communication capabilities and smart grid technologies, load serving entities (LSEs) could offer retail customers coupon incentives via near-real-time information networks to induce demand response for a future period of time in anticipation of intermittent generation ramping and/or price spikes. This scheme is referred to as coupon incentive-based demand response (CIDR). In contrast to the real-time pricing or peak load pricing DR programs, CIDR continues to offer a flat rate to retail customers and also provides them with voluntary incentives to induce demand response. Theoretical analysis shows the benefits of the CIDR scheme in terms of social welfare, consumer surplus, LSE profit, the robustness of the retail electricity rate, and readiness for implementation. The pros and cons are discussed in comparison with existing DR programs. Numerical illustration is performed based on realistic supply and demand data obtained from the Electric Reliability Council of Texas (ERCOT).

Price responsiveness of solar and wind capacity demands

The integration of ICT into power systems has increased the interdependencies between the two systems. The operation of power system depends on several ICT-enabled grid services which manifest the interdependencies. ENTSO-E system state classification is a tool that is widely used by operators to determine the current operational state of the power system. However, it does not adequately describe the impact of ICT disturbances on the operation of the power system. Despite their interconnections, the operational states of both systems have been described separately so far. This paper bridges the well-established ENTSO-E systems state classification with an ICT system state classification, forming a new model considering the state classification of the ICT-enabled grid services. The model is developed by first identifying the ICT-enabled services, remedial actions and the respective performance requirements that are required by the power system. Then the states of these services are specified based on the supporting ICT system. The resulting joint state description shows how performance degradation of ICT-enabled services (introduced by disturbances) can affect the operation of the interconnected power system. Two case studies of such ICT-enabled services, namely state estimation and on-load tap changer control, are investigated in terms of how their operational states affect the states of the power system. A third case study highlights the interdependencies that exist between the services. These case studies demonstrate the interdependencies that exist between power and ICT systems in modern cyber-physical energy systems, thus highlighting the usage of a unified system state description.

Guest Editorial: Situational awareness of integrated energy systems

The modern power system is becoming more complicated due to the ever increasing penetration of power electronics, which is referred to as a power-electronics-dominated system (PEDS). In this case, the analysis, control, and operation of the entire power system should tone with the energy-paradigm transition pace, where the challenges should be properly tackled. After the brief introduction of grid-connected converters, this article explores stability challenges of PEDSs, especially on the low inertia issue and multitimescale characteristics, as well as the dynamics when connected to weak grids. The low inertia issue is considered as one of great challenges that power electronics introduce to the conventional power grid. Accordingly, the stability issues of PEDSs are discussed. The exploration reveals that the multitimescale coupling among various control loops and the mutual effects of multiple converters demand much more attention than ever before. The coordinated control of converters for the global stability of PEDSs is also summarized, part of which offers the possibility to solve the inertia problem. This article serves as an inspiration on potential solutions to these issues. In order to provide a more intuitive impression of the inertia problem in PEDSs, a case study is exemplified to highlight the analysis and discussion.

Power system oscillation is an unavoidable threat to the stability of an interconnected modern power system. The reliable operation of a modern power system is widely related to the dampening of electromechanical low‐frequency oscillations (ELFOs). These ELFOs must be dampened appropriately to maintain the stability and reliability of the system. However, it is relatively difficult to resolve the problem of ELFOs completely with traditional power system stabilizers (PSSs). Consequently, research should be directed towards the development of efficient damping controllers, or PSSs, for power oscillation damping. Motivated from the aforementioned fact, this article presents the design of a proportional integral derivative power system stabilizer (PID‐PSS) via a long short‐term memory neural network (LSTM) based deep neural approach for damping power system oscillations in interconnected power systems. LSTM is used to train the parameters of PID‐PSS. To evaluate the performance of the proposed LSTM based PID‐PSS, diverse test cases under different operating conditions are examined. Further, the performance of the proposed LSTM based PID‐PSS is compared with traditional PSSs through time‐domain simulations. The test cases reveal the desired efficiency achieved by the proposed LSTM based PID‐PSS under diverse loading conditions.

<div class="htmlview paragraph">The Coordinating Research Council (CRC), California Air Resources Board (ARB), and Texas Commission on Environmental Quality sponsored a program to evaluate the effects of air conditioning (AC) operation on vehicle emissions and fuel economy.</div> <div class="htmlview paragraph">Phase 1 of this work measured the effect of AC use with vehicles operated over several standard driving cycles and verified results previously obtained by other investigators. Phase 2 provided a better understanding of the effects of the interactions between ambient conditions (solar load, temperature, and humidity) and AC operation on emissions and fuel economy. The Phase 2 program also verified results of Phase 1 by using a different population of vehicles.</div> <div class="htmlview paragraph">The program results demonstrated that AC operation had a substantial impact on emissions and fuel consumption. Operation of the vehicle AC system over a range of environmental conditions resulted in consistent increases in vehicle emissions of nitrogen oxides (NO<sub>x</sub>) and carbon monoxide (CO). NO<sub>x</sub> increased from 0.1 to 0.6 g/mile, depending on the severity of the test cycle and ambient conditions. CO increased from 0.5 to 12 g/mile. However, analyses indicated that increases in CO were actually due to the increases in ambient temperatures that were employed in the experiments.</div> <div class="htmlview paragraph">The application of solar load to the vehicle increased the impact of AC operation. The application of solar load increased NO<sub>x</sub> emissions from 0.10 to 0.14 g/mile.</div> <div class="htmlview paragraph">Although this experiment was not designed to specifically address fuel economy, operation of the vehicle AC system caused substantial decreases in vehicle fuel economy, ranging from 2.5 to 4.5 miles/gallon. The addition of solar load increased the effect on fuel economy, primarily at less severe ambient conditions. Solar load caused a decrease of 1.3 miles/gallon at 80°F and moderate humidity.</div> <div class="htmlview paragraph">The results of the program were intended to aid in the improvement of vehicle emissions inventory models. NO<sub>x</sub> emission increases due to the use of AC ranged from 15% to 100% of baseline values. This level of effect indicates that AC use should be well accounted for in these models.</div>

In the operation of central AC (air conditioning) systems, the energy consumption of chillers account for a large amount of proportion. Different numbers and capacities of chillers and their auxiliary facilities will lead to different operating energy consumption. A reasonable installation scheme of chillers and their auxiliary facilities is of great significance in reducing the total energy consumption of central AC systems. In this paper, with the help of an existing building located in Baoding, China, dynamic simulation to the operation of a central AC system in summer is carried through. First, the mathematical models of the parameters of the main facilities are established, and the electric power consumption simulation modules of the central AC system are set up by using the Matlab/Simulink tool. Second, four installation schemes of main facilities are introduced and the simulated electric power consumption curves of operation are obtained. Finally, through the analysis to the simulated results, the optimal installation and operation scheme of AC system are proposed. The work will be good references to the system design and operation, as well as the system retrofit of central AC systems to achieve the operating energy conservation.

Large HVAC systems are good candidates for elastic loads in demand side management optimization of smarter grid infrastructures. There is an increaseing trend of using Split-type Air Conditioners (ACs) in residential, commercial and industrial setups for fine granularity temperature control. The traditional control in each unit is through duty-cycle based hysteretic method which is simple but inefficient and requires power spikes at every start-up of the compressor. In developing countries with weak grids, people rely on backup sources like generators and UPS. Large power surge at each turn-on inflicts huge pressure on these constrained resources. Limited work has been reported that aims at energy optimization and controlled operation of Split-type ACs constructed using single phase permanent split capacitor induction machine (SPPSCIM). Our work targets operational optimization, efficiency enhancement and increased load handling capability of contrained power systems in setups employing multiple AC units. This is done through networked Variable Frequency Drives (VFD) for conventional ACs which allow elimination of surge current at turn-on and tighter set-point control while drawing sinusoidal current near unity power factor from the constrained source. Furthermore, the networked operation allows integration of existing installed base of standalone units through Building Management System (BMS) with the central control infrastructure. The prototype networked VFD is tested on a 2.3kW Mitsubishi Electric Split-type AC through Wi-Fi and the results are presented to confirm the expected results in network mode of operation.