Multi-agent systems are well-known for their expressiveness to explore interactions and knowledge representation in complex systems. Multi-agent systems have been applied in the energy domain since the 1990s. As more applications of multi-agent systems in the energy domain for advanced functions, the interoperability raises challenge raises to an increasing requirement for data and information exchange between systems. Therefore, the application of ontology in multi-agent systems needs to be emphasized and a systematic approach for the application needs to be developed. This study aims to investigate literature on the application of ontology in multi-agent systems within the energy domain and map the key concepts underpinning these research areas. A scoping review of the existing literature on ontology for multi-agent systems in the energy domain is conducted. This paper presents an overview of the application of multi-agent systems (MAS) and ontologies in the energy domain with five aspects of the definition of agent and MAS; MAS applied in the energy domain, defined ontologies in the energy domain, MAS design methodology, and architectures, and the application of ontology in the MAS development. Furthermore, this paper provides a recommendation list for the ontology-driven multi-agent system development with the aspects of 1) ontology development process in MAS design, 2) detail design process and realization of ontology-driven MAS development, 3) open standard implementation and adoption, 4) inter-domain MAS development, and 5) agent listing approach.

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Agent-based simulation of the decision-making process for adoption of smart energy solutions can provide evidence for smart energy solutions' providers to decide for a business strategy which results in the best adoption rate. The adoption rate of smart energy solutions is important in achieving climate goals, such as the Danish goal to have 100% renewable electricity production by 2030. This paper shows how agent-based simulation can be used to investigate the decision-making process for adoption of smart energy solutions. The study investigates a case about Danish commercial greenhouse growers' adoption of a demand response program. The simulation outputs an adoption curve and grower information. The results provide the maximum monetary cost for achieving an adoption rate of 50% in 5 years.

Due to the complexity of business ecosystems, the architecture of business ecosystems has not been well discussed in the literature, and modeling or simulation of business ecosystems has been rarely focused. Therefore, this paper proposes a business ecosystem ontology and introduces a methodology for business ecosystem architecture design. The proposed methodology includes five stages: 1) Boundary identification of a business ecosystem; 2) Identification of actors and their roles in the business ecosystem; 3) Identification of actors’ value propositions; 4) Identification of interaction between actors; 5) Verification of business ecosystem architecture design. This paper uses the Danish electricity system as an example to introduce the methodology, and use Electric Vehicle home charging as a case study to demonstrate the application of the developed methodology. The case study demonstrates that the proposed methodology is a systematic approach and can be easily applied to any ecosystem architecture design with the five stages, and the designed ecosystem architecture can represent the physical system and business. Several definitions are clarified in the paper, e.g., actor, role, interaction, ecosystem roadmap and expanded/shifted ecosystem, etc. With clear definitions, the proposed methodology provides a visualized, clear structure of behaviors and specifications for a given business ecosystem.

Changes in brain activity were studied at different depths of isoflurane anaesthesia. Ten healthy women (ASA group I) were investigated during non-critical surgery. Two channels of the EEG were stored on tape simultaneously with alveolar concentration of carbon dioxide, inspired oxygen concentration, mean arterial pressure, ECG and temperature. Signal processing was made offline. Spectral information from 2-s EEG segments was extracted using autoregressive modelling. Repetitive hierarchical clustering was used to define a common learning set of basic patterns. With this learning set, the EEG was classified, and the results presented in a class probability histogram. The basic patterns were related to the clinical depth of anaesthesia in all patients and assigned specific colours. Using this colour code, the class probability histogram showed a high degree of simplicity. Decreasing or increasing the isoflurane concentration caused the same trend in the class profile in all patients. This indicates that the EEG pattern might be a sensitive tool for decision making during administration of general anaesthetics.

As wind and solar power displace conventional generation in the electricity grid, there is an urgent need for technologies that can deal with the variation in supply. Demand response technology has been proposed as a solution to make the demand-side flexible and able to effectively adjust to variations in supply. However, consumers do not simply invest in demand response technologies without insurance that their investment will pay back. This paper applies agent-based simulation to investigate consumer adoption behaviour of implicit demand response solutions that allow consumers to adjust their electricity use to the hourly prices in day-ahead spot markets. The simulation uses the case study of a water supply system to show that the adoption speed of implicit demand response technology depends on both technical characteristics of the system and the business model of the implicit demand response solution provider. Hence, this paper contributes with insight on how agent-based simulation can help technology providers to design solutions which match the needs of system operators.

The increasing number of distributed energy resources in the distribution grids creates the risk of grid congestion and the high cost of grid expansion. The implementation of the dynamic distribution grid tariffs can potentially avoid grid congestion. Meanwhile, the design and implementation of any distribution tariff need to consider and match the regional/national requirements. However, there is no sufficient evaluation method available to review and evaluate the feasibility of the dynamic distribution tariffs. Therefore, this paper introduces a feasibility evaluation method with four dimensions of technical, economic, social, and regulatory to review dynamic distribution tariffs. The literature on dynamic distribution tariffs is collected, and 29 dynamic distribution tariffs are selected and further categorized into five attributes of rationale, cost drivers, dynamics, events, and active demand. The evaluation results show that the time-of-use tariff is the most feasible dynamic distribution tariff, and the review of a proposed future distribution tariff model in Denmark verifies the evaluation method and results. The developed feasibility evaluation method for dynamic distribution tariffs can ensure the design and implementation of a dynamic distribution tariff to be feasible and applicable in a region.

Commercial greenhouses in Denmark account for 0.7% of the total Danish electricity consumption in 2017. 75% of the consumption is estimated to come from artificial light. Artificial light in the greenhouses is identified as a major potential to provide energy flexibility through demand response. Energy flexibility in greenhouses is depending on the electricity price and weather conditions as artificial light is managed in relation to natural light and hourly electricity prices. Therefore, this paper investigates commercial greenhouse growers' adoption behavior of implicit demand response enabled software solutions in different geographical regions with different weather conditions and electricity markets. The simulation of the greenhouses is developed as an agent-based system. The paper uses Denmark as the baseline model, the UK for market comparison with Denmark, and Spain and Denmark for analyzing the adoption rate's sensitivity to solar irradiation. The results reveal that higher electricity prices and more solar irradiation result in faster adoption and the adoption rate is more sensitive to electricity price than solar irradiation.