Load Management Capabilities Research Articles

Performance analysis of power conditioning and distribution module for microsatellite applications

Abstract Algeria’s micro-satellite, Alsat-1b, was successfully launched into a 680 km low Earth orbit onboard a PSLV-C35 rocket from Sriharikota, South India, on September 26, 2016. The spacecraft was conceived, built and launched as part of an 18-month technology transfer programme between Algeria’s Algerian Space Agency (ASAL) and the United Kingdom’s Surrey Satellite Technology Limited (SSTL). This document details the Power Conditioning and Distribution Module’s (PCM-PDM) design and performance in orbit, critical component of a satellite electrical power system, responsible for converting, regulating and distributing power to various subsystems and payloads. The PCM-PDM developed and produced by SSTL was subjected to rigorous testing simulating harsh space conditions to assess its performance. The results of this comprehensive analysis indicate that the module can effectively withstand extreme environmental factors and function optimally in challenging settings. The analysis focused on the PCM-PDM’s ability to provide reliable and efficient power conditioning and distribution to the satellite, including its load management capabilities, overcurrent protection, protection against undervoltage and critical mode operations. The results of the performance analysis showed that the PCM-PDM met the required specifications and demonstrated reliable and efficient operation in different modes of the satellite’s mission. The study highlights the importance of careful design and rigorous testing of the PCM-PDM to ensure the reliable and efficient operation of the satellite and its payloads.

A novel and failsafe blockchain framework for secure OTA updates in connected autonomous vehicles

Connected Autonomous Vehicles (CAVs) are becoming data centers on wheels, amassing petabytes of data. They require a combination of software and hardware systems to operate reliably and simply in real-time. However, due to the transient nature of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) networks and CAVs' dependency on a connected software system, we need a new paradigm for secure and immutable data exchange, which emulates smart contracts. For this reason, CAV manufacturers need to maintain the latest version of software to protect CAVs from system failure and cyber threats in real-time, which is difficult with traditional centralized management. The key challenges that we must overcome are the authentication and secure sharing of the Over-The-Air (OTA) software updates and the scalability of distribution at the level of CAVs.This paper proposes a secure and scalable software updates framework in a distributed manner for the CAVs, leveraging Blockchain (BC) with smart contract technology. The framework is able to overcome slow processing speed, which is one of the significant limitations of BC in terms of lower latency and less packet overhead with a high level of security against possible cyber-attacks. We use a salting-based hashing scheme over the traditional Elliptic Curve Cryptography (ECC) Key. This process ensures multi-factor authenticated protection from malicious transactions while downloading and installing any new feature update in CAVs. Moreover, by using Hyperledger BC, our framework provides cost-free transactions while successfully upgrading and deploying OTA software patches in any system of CAVs. This paper extends our previous work [51] by analysing our proposed BC framework's immutability and load management capability. Our simulation results show that our proposed framework is robust, secure, and failsafe for CAV manufacturers and end-users.

Detection of Load-Altering Cyberattacks Targeting Peak Shaving Using Residential Electric Water Heaters

The rapid adoption of the smart grid’s nascent load-management capabilities, such as demand-side management and smart home systems, and the emergence of new classes of controllable high-wattage loads, such as energy storage systems and electric vehicles, magnify the smart grid’s exposure to load-altering cyberattacks. These attacks aim at disrupting power grid services by staging a synchronized activation/deactivation of numerous customers’ high-wattage appliances. A proper defense plan is needed to respond to such attacks and maintain the stability of the grid, and would include prevention, detection, mitigation, incident response, and/or recovery strategies. In this paper, we propose a solution to detect load-altering cyberattacks using a time-delay neural network that monitors the grid’s load profile. As a case study, we consider a cyberattack scenario against demand-side management programs that control the loads of residential electrical water heaters in order to perform peak shaving. The proposed solution can be adapted to other load-altering attacks involving different demand-side management programs or other classes of loads. Experiments verify the proposed solution’s efficacy in detecting load-altering attacks with high precision and low false alarm and latency.

Soft topological lattice wheels

We investigate the large deformation and extreme load-management capabilities of a soft topologically polarized kagome lattice mapped to a cylindrical domain through the problem of a lattice wheel rolling on an irregular surface. We test the surface–lattice interaction experimentally by subjecting a 3D-printed topological lattice wheel prototype to localized and distributed boundary loads. This investigation reveals a dichotomy in the force transfer between the two loading scenarios, whereby localized loads are absorbed with limited stress penetration into the bulk and small force transfer to the wheel axle, compared to distributed loads. Through numerical simulations, we compare the lattice wheel against a baseline solid wheel to highlight the unique stress management opportunities offered by the lattice configuration. These findings promote the design of rolling objects enabled by topological mechanics, in which a surplus of softness, activated by local asperities, can coexist with a globally stiff response to distributed loads that ensures satisfactory load-bearing capabilities.

An assessment of the load modifying potential of model predictive controlled dynamic facades within the California context

California is making major strides towards meeting its greenhouse gas emission reduction goals with the transformation of its electrical grid to accommodate renewable generation, aggressive promotion of building energy efficiency, and increased emphasis on moving toward electrification of end uses (e.g., residential heating, etc.). As a result of this activity, the State is faced with significant challenges of systemwide resource adequacy, power quality and grid reliability that could be addressed in part with demand responsive (DR) load modifying strategies using controllable building technologies. Dynamic facades have the ability to potentially shift and shed loads at critical times of the day in combination with daylighting and HVAC controls. This study explores the technical potential of dynamic facades to support net load shape objectives. A model predictive controller (MPC) was designed based on reduced order thermal (Modelica) and window (Radiance) models. Using an automated workflow (involving JModelica.org and MPCPy), these models were converted and differentiated to formulate a non-linear optimization problem. A gradient-based, non-linear programming problem solver (IPOPT) was used to derive an optimal control strategy, then a post-optimization step was used to convert the solution to a discrete state for facade actuation. Continuous state modulation of the façade was also modeled. The performance of the MPC controller with and without activation of thermal mass was evaluated in a south-facing perimeter office zone with a three-zone electrochromic window for a clear sunny week during summer and winter periods in Oakland and Burbank, California. MPC strategies reduced total energy cost by 9–28% and critical coincident peak demand was reduced by up to 0.58 W/ft2-floor or 19–43% in the 4.6 m (15 ft) deep south zone on sunny summer days in Oakland compared to state-of-the-art heuristic control. Similar savings were achieved for the hotter, Burbank climate in Southern California. This outcome supports the argument that MPC control of dynamic facades can provide significant electricity cost reductions and net load management capabilities of benefit to both the building owner and evolving electrical grid.

Load Compensator with an Energy Storage for a Grid Connected PV Based Active Generator

At present, in order to build a sustainable economy with less greenhouse emission, the attention has been drawn towards the smart grid concept. In this context, grid connected photovoltaic (PV) systems play a major role. When it comes to grid connected PV systems, the intermittent nature of PV generation and the uncontrolled power injection may cause excess variations in the grid voltage, frequency and the power quality. Therefore, the concept of active generator with innovative energy management strategies is a good alternative with which the PV generated power can be injected to the grid in a controlled manner while achieving the output power quality, instantaneous power, balance, frequency control and voltage control. This work proposes an innovative architecture for a grid connected PV based active generator with multi sources; a PV array, battery and the grid supply which are connected to a common AC bus as well as it analyses the innovative load flow control strategy associated with the proposed architecture. To make sure an uninterruptable power supply to the load, PV arrays inject the generated power in maximum power point tracking mode and the battery compensates the power requirement in coordination with the grid while maintaining the frequency stability within the system. Therefore, this paper analyses the performance of the energy controller and the load management capability of the proposed system while achieving the frequency control.

Demand-side improvement of short-term load forecasting using a proactive load management – a supermarket use case

The future electricity grid will highly depend on cooperation between the supply- and demand-side. One possibility for cooperation is to involve demand-side end-users in a day-ahead load planning process. The end-users could provide their short-term load forecast (STLF) by incorporating local knowledge, which is usually not available to the load aggregators. Moreover, local load management capabilities could even provide a means to decrease the difference between the previously announced load profile and actual consumption. The article demonstrates how the STLF and proactive load management approach could be effectively combined for the supermarket use case. The use case shows that even with the simple STLF predictor, which incorporates the seasonal and regression model parts, a reasonably accurate model can be provided. Moreover, the demand shifting capabilities of the refrigeration systems enable for the deviations from the forecasted demand to be effectively reduced, which consequently increases the accuracy of the previously announced demand plan. Such an approach could be extended to various end-users for which the high STLF accuracy would provide a means to either directly participate in the electricity market, participate in some novel (non)cooperative trading schemes, or to directly support the load aggregators.

A Distribution Automation Demonstration Project

A multi-functional distribution automation system, with load management capability, being installed as a demonstration project, is described. The purposes to be served by this project are discussed, and information to be retrieved is identified.

A Distribution Automation Demonstration Project

A multi-functional distribution automation system, with load management capability, being installed as a demonstration project, is described. The purposes to be served by this project are discussed, and information to be retrieved is identified.