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This paper investigates the cyber resiliency of future power systems with high penetration of distributed energy resources using advanced distributed and (or) hierarchical control architectures. Specifically, we simulate cyberattacks on three prototypical use cases, and we identify attack scenarios that are the most damaging to the overall system performance. We show that these attacks can have a significant impact on grid operation. Results provide additional insights into the robustness of the system to the most common cyberattacks. • This paper delves into the realm of cyber-resiliency, examining its implications across three distinct use cases encompassing distributed, distributed-hierarchical, and hierarchical control architectures. • Through exhaustive numerical simulations, we discern and highlight the precise signals within the system that exhibit heightened susceptibility to cybersecurity attacks. • The results obtained from our analysis offer valuable insights into the robustness of these systems, shedding light on their ability to withstand potential cybersecurity threats. [ABSTRACT FROM AUTHOR]

To explore the bidirectional interaction between renewable energy and buildings in multi-agent energy systems, this paper proposes a distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings based on chance-constrained programming (CCP). First, the multi-agent energy system integrated with wind, solar, and buildings is comprehensively modeled with detailed electric and thermal characteristics for flexibility enhancement. Then for maximizing the profits of the cooperative energy system and each engaged agent, a Nash bargaining model is presented and is divided into two subproblems: the coalition income and the power payment. To preserve the privacy of agents, the adaptive alternating direction method of multipliers (ADMM) is exploited to solve both subproblems. Meanwhile, the CCP method is applied to address diverse uncertainties from wind and solar power generation as well as outdoor temperature. Finally, the effectiveness of the proposed strategy is validated. The simulation results show that, besides the privacy of information among all agents being well preserved, our strategy enhances the profits not only for the energy system but also for all engaged agents. • A distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings is proposed. • Bidirectional interactions between wind/solar power plant and buildings are realized by the Nash bargaining-based incentive cooperative mechanism. • Indoor temperature comfort in buildings is ensured under diverse uncertainties by exploring the building flexibility through chance-constrained programming. • Power trading results and the economy of the energy system and the participating agents are analyzed. [ABSTRACT FROM AUTHOR]

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today's conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure. [ABSTRACT FROM AUTHOR]

An increasing number of federal and state regulations and financial incentive programs have been enacted in recent years to promote renewable energy systems integration in new buildings. This requires designers to analyze the initial and lifecycle costs of feasible renewable energy systems in order to optimize their integration in buildings during all design phases. The goal of this study is to support designers in generating optimal building schematic designs to maximize the integration and generation of onsite renewable energy. To accomplish this goal, this paper presents the development of a novel multi-objective optimization model that can be used during the schematic design phase to identify optimal building design decisions including dimensions, orientation, location on site, solid-to-void ratio, and the type and size of onsite renewable energy systems. The optimization objectives of the developed model focus on maximizing the generation of onsite renewable energy, maximizing savings-to-investment ratio of all integrated renewable energy, and minimizing both construction and renewable energy costs. An application example of optimizing the schematic design of a 6500 m2 building was analyzed to highlight the model original contributions and its novel capabilities. The results of this analysis highlight the original contributions of the developed model and its novel capabilities that can be used to maximize the generation of onsite renewable energy and its savings-to-investment ratio while minimizing building cost by optimizing building schematic design and renewable energy (RE) technology decisions. • Optimizing building schematic design with renewable energy integration. • Maximizing RE generation, savings-to-investment ration, and minimizing bilding costs. • Complying with building design requirements and renewable energy constraints. • Optimal building design parameters and renewable energy technologies. • Analyzing the impact of design decisions on renewable energy and building costs. [ABSTRACT FROM AUTHOR]

A two-stage robust optimization (RO) method for buildings' space heating loads (SHLs) in an integrated community energy system (ICES) is proposed. At the first stage, a bi-level optimization is deployed to formulate the hierarchical relationship between the ICES operator and consumers in buildings to enable the optimal heating pricing strategy between them. Thermal inertia of SHLs, which is modelled utilizing the Resistor-Capacitor thermal network, is used to provide heating demand response according to the optimal heating sale prices released by the ICES operator. At the second stage, the ICES operator decides the optimal energy purchase schedules from the upper energy systems after the heating sale prices are decided at the first stage. Since the day-ahead energy prices differ from the real-time ones, a RO method is adopted. The original min–max RO problem is converted into its dual problem to cooperate with the bi-level optimization. Finally, the whole optimization model is transformed into a mixed integer linear programming (MILP) based on Karush-Kuhn-Tucker conditions, duality theory, big-M method and piecewise linearization. Numerical results show that the proposed model can balance the interests between the ICES operator and consumers. And the profit of the ICES operator is 5.10% higher using RO method, compared to the deterministic optimization under energy prices uncertainties. Compared to the benchmark solution method, the computation efficiency of the final MILP model in this paper is highly improved and the convergency of the MILP model can be guaranteed. [ABSTRACT FROM AUTHOR]

• A novel Dendrite Net based on the adaptive mean square gradient is proposed. • Energy consumption prediction and analysis model of buildings is obtained based on energy consumption data of buildings. • The prediction accuracy of the proposed model is higher than most neural network methods. • The optimization direction of building envelopes is found for reducing energy consumption. • The maximum carbon dioxide emissions can be reduced by 158.17 kg. Artificial neural networks have been widely applied in construction industries. Due to dendrites of biological neurons participating in the pre-calculation of neural networks, the structure of the traditional artificial neural network needs to be adjusted subjectively. Thus, a novel dendrite net based on the adaptive mean square gradient is proposed in this paper. The energy consumption of buildings is predicted and analyzed by the proposed method to cut the carbon dioxide emissions. The adaptive mean square gradient method is used to update the weight matrix of the dendrite net method, which can avoid errors caused by selecting hidden layer nodes to improve the prediction accuracy of the proposed method. Finally, the proposed method is used to energy saving and emission reducing of the construction industry. Compared with other methods, the experimental results show the availability of the proposed method. Through predicting the heating and cooling loads based on the proposed method, the construction plan is adjusted to decrease the energy consumption of buildings. Moreover, the appliances energy consumption is predicted and analyzed by the proposed method to improve energy efficiency and cut carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

• Temporal variation of environmental impacts related to buildings electricity consumption is modelled. • Short term (e.g. peak demand) and long term aspects (e.g. variation of production capacities) are accounted for. • The method was applied in a case study, addressing uncertainties. • Consequences of electrification in the building sector can be evaluated. • Electrification does not necessarily reduce greenhouse gases emissions. The building stock is a major contributor to energy consumption and greenhouse gases emissions (GHG), which can be evaluated using life cycle assessment (LCA). Electrification of buildings, e.g. replacing fuel and gas boilers with heat pumps, in order to reduce these emissions is often seen as an option, but this will have short term effects by increasing peak demand, and long term effects by requiring more electricity production capacities. In this paper, a methodology to account for such interaction in LCA is presented. It connects three models addressing: market allocation on a national scale over a long term period, short term variation (i.e. seasonal, daily and hourly) of the electricity mix also on a national scale, and building energy simulation at the scale of one building. This methodology has been applied to a case study including a sample of buildings in the French context, but it can be used in other countries. Six buildings have been studied over 100 years considering 50 energy transition scenarios. Results show that the environmental impacts vary more depending on the scenarios than on the types of the building. Marginal mixes considered in consequential LCA are mainly composed of coal, gas, nuclear and peak technology production which explains the highest values of the different impacts compared to average mixes used in attributional LCA. This approach allows to address uncertainties related to electricity production. [ABSTRACT FROM AUTHOR]