Demand side load management using a three step optimization methodology

In order to keep a proper functional electricity grid and to prevent large investments in the current grid, the creation, transmission and consumption of electricity needs to be controlled and organized in a different way as done nowadays. Smart meters, distributed generation and -storage and demand side management are novel technologies introduced to reach a sustainable, more efficient and reliable electricity supply. Although these technologies are very promising to reach these goals, coordination between these technologies is required. It is therefore expected that ICT is going to play an important role in future smart grids. In this paper, we present the results of our three step control strategy designed to optimize the overall energy efficiency and to increase the amount of generation based on renewable resources with the ultimate goal to reduce the CO2 emission resulting from generation electricity. The focus of this work is on the control algorithms used to reshape the energy demand profile of a large group of buildings and their requirements on the smart grid. In a use case, steering a large group of freezers, we are able to reshape a demand profile full of peaks to a nicely smoothed demand profile, taking into the account the amount of available communication bandwidth and exploiting the available computation power distributed in the grid

Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics

Wicked problems like sustainable energy and financial market stability are societal challenges that arise from complex socio-technical systems in which numerous social, economic, political, and technical factors interact. Understanding and mitigating them requires research methods that scale beyond the traditional areas of inquiry of Information Systems (IS) “individuals, organizations, and markets” and that deliver solutions in addition to insights. We describe an approach to address these challenges through Competitive Benchmarking (CB), a novel research method that helps interdisciplinary research communities to tackle complex challenges of societal scale by using different types of data from a variety of sources such as usage data from customers, production patterns from producers, public policy and regulatory constraints, etc. for a given instantiation. Further, the CB platform generates data that can be used to improve operational strategies and judge the effectiveness of regulatory regimes and policies. We describe our experience applying CB to the sustainable energy challenge in the Power Trading Agent Competition (Power TAC) in which more than a dozen research groups from around the world jointly devise, benchmark, and improve IS-based solutions

Deriving Digital Energy Platform Archetypes for Manufacturing – A Data-Driven Clustering Approach

External factors such as climate change and the current energy crisis due to global conflicts are leading to the increasing relevance of energy consumption and energy procurement in the manufacturing industry. In addition to the growing call for sustainability, companies are increasingly struggling with rising energy costs and the reliability of the power grid, which endangers the competitiveness of companies and regions affected by high energy prices. Appropriate measures for energy-efficient and, not least, energy-flexible production are necessary. In addition to innovations and optimizations of plants and processes, digital energy platforms for the visualization, analysis, optimization, and control of energy flows are becoming essential. Over time, several digital energy platforms emerged on the market. The number and the different functionalities of the platforms make it challenging for classic manufacturing companies to keep track and select the right digital energy platform. In literature, the characteristics and functionalities of digital energy platforms have already been identified and structured. However, a classification of existing platforms into archetypes makes it easier for companies to select the platforms providing the missing functionality. To tackle this issue, we conducted an explorative and data-driven cluster analysis based on 49 existing digital energy platforms to identify digital energy platform archetypes and derive implications for research and practice. The results show five different archetypes that differ primarily in terms of functionalities on energy market integration. The identified archetypes provide a well-founded overview of the similarities and differences of digital energy platforms. Decision makers in manufacturing companies will benefit from the archetypes in future analyses as decision support in procurement processes and modifications of digital energy platforms

Cyber-physical energy systems modeling, test specification, and co-simulation based testing

The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specification method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification

Characterization of new flexible players: Deliverable D3.2

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The subject matter of this report is the analysis of the electricity markets’ actors’ scene, through the identification of actor classes and the characterisation of actors from a behavioural and an operational perspective. The technoeconomic characterization of market participants aims to support the upcoming model enhancements by aligning the agent-based model improvements with the modern market design challenges and the contemporary characteristics of players. This work has been conducted in the context of task T3.2, which focuses on the factorization of the distinctive operational and behavioural characteristics of players in market structures. Traditional parties have been considered together with new and emerging roles, while special focus has been given on new actors related to flexible technologies and demand-side response. Among the main objectives have been the characterization of individual behaviours, objectives and requirements of different electricity market players, considering both the traditional entities and the new distributed ones, and the detailed representation of the new actors.N/

A generic holonic control architecture for heterogeneous multi-scale and multi-objective smart microgrids

Designing the control infrastructure of future “smart” power grids is a challenging task. Future grids will integrate a wide variety of heterogeneous producers and consumers that are unpredictable and operate at various scales. Information and Communication Technology (ICT) solutions will have to control these in order to attain global objectives at the macrolevel, while also considering private interests at the microlevel. This article proposes a generic holonic architecture to help the development of ICT control systems that meet these requirements. We show how this architecture can integrate heterogeneous control designs, including state-of-the-art smart grid solutions. To illustrate the applicability and utility of this generic architecture, we exemplify its use via a concrete proof-of-concept implementation for a holonic controller, which integrates two types of control solutions and manages a multiscale, multiobjective grid simulator in several scenarios. We believe that the proposed contribution is essential for helping to understand, to reason about, and to develop the “smart” side of future power grids

Reactive point processes: A new approach to predicting power failures in underground electrical systems

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures ("manhole events"), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A review of hierarchical control for building microgrids

Building microgrids have emerged as an advantageous alternative for tackling environmental issues while enhancing the electricity distribution system. However, uncertainties in power generation, electricity prices and power consumption, along with stringent requirements concerning power quality restrain the wider development of building microgrids. This is due to the complexity of designing a reliable and robust energy management system. Within this context, hierarchical control has proved suitable for handling different requirements simultaneously so that it can satisfactorily adapt to building environments. In this paper, a comprehensive literature review of the main hierarchical control algorithms for building microgrids is discussed and compared, emphasising their most important strengths and weaknesses. Accordingly, a detailed explanation of the primary, secondary and tertiary levels is presented, highlighting the role of each control layer in adapting building microgrids to current and future electrical grid structures. Finally, some insights for forthcoming building prosumers are outlined, identifying certain barriers when dealing with building microgrid communities