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).

Estimating the Potential for Thermal Load Management in Buildings at a Large Scale: Overcoming Challenges Towards a Replicable Methodology

Guest Editorial: Situational awareness of integrated energy systems

Design Management Journal (Former Series)Volume 2, Issue 4 p. 81-84 Interring the Great Myth: The Ethics of ENERGY EFFICIENCY Terence P. Falvey, Terence P. Falvey Terence P. Falvey, P.E., C.C.P., C.E.M., is a consulting engineer whose practice addresses the management and efficient use of large thermal and electrical energy loads. His projects span the range from Energy Audits to Cogeneration Feasibility Studies for a diverse client mix.Search for more papers by this author Terence P. Falvey, Terence P. Falvey Terence P. Falvey, P.E., C.C.P., C.E.M., is a consulting engineer whose practice addresses the management and efficient use of large thermal and electrical energy loads. His projects span the range from Energy Audits to Cogeneration Feasibility Studies for a diverse client mix.Search for more papers by this author First published: Fall 1991 https://doi.org/10.1111/j.1948-7169.1991.tb00094.x AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume2, Issue4Fall 1991Pages 81-84 RelatedInformation

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.

In modern power systems, there is a greater penetration of renewable sources, resulting in the expansion of microgrid. By implementing RES instead of conventional synchronous machines, the system’s overall inertia is significantly reduced. Modern and future power systems must reduce system inertia and keep the frequency at its nominal value since frequency oscillation impacts on the functionality, stability, and resilience of the system. Therefore, creating a stable, scalable, and reliable virtual inertia control system is crucial to effectively minimize the deviations during significant contingencies. Consequently, taking into account the probable issues, the foremost objective of this research work is to improve the dynamic security of an island microgrid through the implementation of a frequency control concept based on virtual inertia control. In the research study, the proposed microgrid system comprises of photovoltaics, wind-generating units, thermal power units, storage units, electric vehicles (EVs), and loads. A cascaded PIDFN controller is optimally designed using a novel metaheuristic modified differential evolution (MDE) algorithm that regulates the frequency based on virtual inertia control. In the MATLAB®/SIMULINK environment, various operating situations such as load variations, RES, and EVs disconnection are investigated, and the performance of the VIC-based MDE controller was compared to that of other controllers based on evolutionary optimization algorithms such as DE and TLBO. The results validate that the recommended virtual inertia control strategy enhances the reliability of the system.

Hydrogen as a fuel exhibits significant potential for integration into energy systems, potentially enabling the decarbonization of the transportation sector and achieving sustainability goals in alignment with global and European Sustainable Development Goals strategies (SDGs). The decarbonization of the energy sector is outlined in the Paris Agreement, which sets specific objectives that United Nations member states are obligated to meet by 2030 and 2050. This paper explores the latest trends in the development and application of hydrogen in internal combustion engines (H₂ - ICE) as a fuel. It covers the fundamental principles of H₂ - ICE operation, including necessary modifications to conventional internal combustion engines (modification of fuel supply systems, combustion chamber design, thermal load management, and H₂ storage systems in vehicles). Additionally, the paper examines the compliance of H₂ - ICE technology with European strategies, including the European Green Deal and Euro 7 emission standards. The European Union's regulatory framework (Hydrogen Strategy and REPowerEU plan) defines guidelines for accelerating the transition to renewable energy sources and achieving energy independence, supported by financial mechanisms that encourage the adoption of hydrogen in the automotive industry. Furthermore, the study provides an analytical overview of industry trends among leading vehicle and engine manufacturers, highlighting the development processes of hydrogen engines and their applications in passenger and commercial vehicles. Through an analysis of the advantages and limitations of H₂ - ICE, the paper positions H₂ - ICE as a complementary technology to fuel cells (hybrid electric vehicles) and offers specific conclusions and recommendations for future research, including improvements in engine efficiency, emission reductions, and the development of hydrogen infrastructure. These efforts aim to ensure a sustainable and economically viable transition toward clean transportation.

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.