Anders Clausen

Demand response in commercial buildings with an Assessable impact on occupant comfort

Electricity grids are facing challenges due to peak consumption and renewable electricity generation. In this context, demand response offers a solution to many of the challenges, by enabling the integration of consumer side flexibility in grid management. Commercial buildings are good candidates for providing flexible demand due to their volume and the stability of their loads. However, existing technologies and strategies for demand response in commercial buildings fail to enable services with an assessable impact on load changes and occupant comfort. In this paper we propose the ADRALOC system for Automated Demand Response with an Assessable impact on Loads and Occupant Comfort. This enhances the quality of demand response services from a grid management perspective, as these become predictable and trustworthy. At the same time building managers and owners can participate without worrying about the comfort of occupants. We present results from a case study in a real office building where we illustrate the advantages of the system (i.e., load sheds of 3kW within comfort limits). Presenting a better system for demand response in commercial buildings is a step towards enabling a higher penetration of intelligent smart grid solutions in commercial buildings.

Load management of data centers as regulation capacity in Denmark

Replacing the traditional fossil-based electricity generation with clean renewable energy is critical to address carbon emissions and climate change in particular. Denmark has a particularly aggressive strategy for renewable energy generation. By 2020 50% of electricity production is to be wind based and by 2050 the goal is to have an energy production based entirely on renewable energy. Renewable energy such as solar and wind is subject to variations due to changing weather conditions. This requires additional balancing capacity and ancillary services in order to balance the grid for transmission system operators and distribution system operators and balance errors in forecasts made by balance responsible parties. By enabling the demand-side to adapt consumption to match power generation, we can address this in a cost-effective and environmental sound way. In this context, data centers are of special interest as they account for 500 GWh of consumption in Denmark or nearly 2% of the total electricity consumption. This paper performs an analysis on load management capabilities of data centers in Denmark based on the experiences in the U.S. We characterize the load management capabilities of the data centers based on their types, technology, and their application as grid management resources. Further, we identify demand-side entry barriers towards market participation. Our findings suggest that groups of data centers can offer dynamic load flexibility as virtual power plants, and thereby support the evolution of the Danish energy systems towards its 2020 and 2050 goals.

Supercomputing Centers and Electricity Service Providers: A Geographically Distributed Perspective on Demand Management in Europe and the United States

Supercomputing Centers (SCs) have high and variable power demands, which increase the challenges of the Electricity Service Providers (ESPs) with regards to efficient electricity distribution and reliable grid operation. High penetration of renewable energy generation further exacerbates this problem. In order to develop a symbiotic relationship between the SCs and their ESPs and to support effective power management at all levels, it is critical to understand and analyze how the existing relationships were formed and how these are expected to evolve.

Load Management Through Agent Based Coordination of Flexible Electricity Consumers

Demand Response (DR) offers a cost-effective and carbon-friendly way of performing load balancing. DR describes a change in the electricity consumption of flexible consumers in response to the supply situation. In DR, flexible consumers may perform their own load balancing through load management (LM) mechanisms. However, the individual amount of load balancing capacity exhibited by the majority of flexible consumers is limited and as a result, coordinated LM of several flexible electricity consumers is needed in order to replace existing conventional fossil based load balancing services. In this paper, we propose an approach to perform such coordination through a Virtual Power Plant (VPP)[1]. We represent flexible electricity consumers as software agents and we solve the coordination problem through multi-objective multi-issue optimization using a mediator-based negotiation mechanism. We illustrate how we can coordinate flexible consumers through a VPP in response to external events simulating the need for load balancing services.

Demand Response Integration Through Agent-Based Coordination of Consumers in Virtual Power Plants

The transition towards an electricity grid based on renewable energy production induces fluctuation in electricity generation. This challenges the existing electricity grid design, where generation is expected to follow demand for electricity. In this paper, we propose a multi-agent based Virtual Power Plant design that is able to balance the demand of energy-intensive, industrial loads with the supply situation in the electricity grid. The proposed Virtual Power Plant design uses a novel inter-agent, multi-objective, multi-issue negotiation mechanism, to coordinate the electricity demands of industrial loads. Coordination happens in response to Demand Response events, while considering local objectives in the industrial domain. We illustrate the applicability of our approach on a Virtual Power Plant scenario with three simulated greenhouses. The results suggest that the proposed design is able to coordinate the electricity demands of industrial loads, in compliance with external Demand Response events.

An Agent-Based Framework for Aggregation of Manageable Distributed Energy Resources

Distributed energy resources (DER) offer an economically attractive alternative to traditional centralized generation. However, the unpredictable and scattered nature of DER prevents them from replacing traditional centralized generator capacity. Aggregating DER under a virtual power plant (VPP) addresses this issue by exposing the combined capabilities of connected DER as a single controllable entity towards the utility. In this paper we propose an architecture that supports multi-level aggregation of DER under VPPs. In this architecture, DER submit energy profiles to a VPP. The VPP may then control the DER within the boundaries defined in the energy profiles. The proposed architecture is hosted in an agent-based framework, Controleum, and is to be demonstrated at a primary school in Denmark, first quarter 2014.

Agent-based Integration of Complex and Heterogeneous Distributed Energy Resources in Virtual Power Plants

A Virtual Power Plant aggregates several Distributed Energy Resources in order to expose them as a single, controllable entity. This enables smaller Distributed Energy Resources to take part in Demand Response programs which traditionally only targeted larger consumers. To date, models for Virtual Power Plants have considered Distributed Energy Resources as simple, atomic entities. However, often Distributed Energy Resources constitute complex and heterogeneous entities with a mix of multiple, controllable loads, generators and electrical storage units which must be coordinated locally. This paper proposes an agent-based method for integration of complex, heterogeneous Distributed Energy Resources into Virtual Power Plants. The approach models Distributed Energy Resources and Virtual Power Plants as agents with multi-objective, multi-issue reasoning. This enables modeling of VPPs constituting complex and heterogeneous Distributed Energy Resources with multiple, local objectives and decision points. The properties of the approach are illustrated using different Virtual Power Plant scenarios, which include Distributed Energy Resources of various types and complexities.

Termination Criteria in Evolutionary Algorithms: A Survey

Over the last decades, evolutionary algorithms have been extensively used to solve multi-objective optimization problems. However, the number of required function evaluations is not determined by nature of these algorithms which is often seen as a drawback. Therefore, a robust and reliable termination criterion is needed to stop the algorithm. There is a huge amount of knowledge encapsulated in the studies targeting termination criteria in evolutionary algorithms, but an updated integrated overview of this knowledge is missing. For this reason, we aim to conduct a systematic research through a comprehensive literature study. We extended the basic categorization of termination criteria to a more advanced one that takes the most common used termination criteria into consideration based on their specifications and the way they have been evolved over time. The survey is concluded by suggesting a road-map for future research directions.

An agent-based negotiation approach for balancing multiple coupled control domains

Solving multi-objective multi-issue negotiation problems involving interdependent issues distributed among multiple control domains is inherent to most non-trivial cyber-physical systems. In these systems, the coordinated operation of interconnected subsystems performing autonomous control is essential to achieve overall system goals. In spite of its importance, the area has received limited attention to date. In this paper, we propose an innovative agent-based coordination approach for coordinating and controlling interconnected subsystems in cyber-physical systems with interdependent issues. The proposed approach can solve negotiation problems with interdependent issues across multiple coupled control domains. We demonstrate our approach by solving a coordination problem where a Combined Heat and Power Plant must allocate electricity for three commercial greenhouses to ensure the required plant yield. Our results show, that our approach is able to balance the individual requirements of multiple coupled control domains and thereby achieve a global equilibrium state for energy allocation and demand.