Distributed Energy Research Articles

Agent-based power management in apartment buildings: Tenant preferences and distributed energy resources

Apartment buildings (APBs) consist of multiple residential spaces and common facilities, unlike single-family buildings, and have correspondingly diverse energy demands; however, household preferences and behaviors have received little attention so far. Therefore, bidirectional flows between households and the APB must be considered to manage the APB energy demands by considering dynamic relationships built by households with preferences and behaviors. This study presents a demand-side management system called the unified home energy management system (U-HEMS), which considers distributed energy resources (DERs) for the APB. The U-HEMS was developed by bidirectional flows of management and decision-making processes for households and the APB. In the management process from the household to the APB, the APB power demands were optimized using a two-stage system, whereas in the decision-making process from the APB to the households, the next actions expressed by weighting factors of the household side were determined for the management process based on an agent-based model. The functionality and performance of the U-HEMS were compared with those of conventional approaches for a 100-unit APB, and five case studies were conducted to analyze their power demands considering different aspects: decision-making process, combinations of households’ preferences, reward system, penetration of electric vehicles, and DER capacities. The results reveal that the U-HEMS effectively reduces the overall costs and total peak power demand because a bidirectional model considers the dynamic relationships between households in the APB and DER operations subject to the time-varying pricing policy. Further studies are needed to assess seasonal effects on APB demands.

A Novel Optimal Planning and Operation of Smart Cities by Simultaneously Considering Electric Vehicles, Photovoltaics, Heat Pumps, and Batteries

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

Polydopamine nanoparticles reinforced injectable hydrogels for the efficient adsorption and catalysis in the wastewater treatment

By in situ generation of hydrazone bonds, a composite hydrogel that combines poly(N-isopropylacrylamide) and carboxymethyl cellulose and is reinforced with PDA nanoparticles was successfully prepared for injectable use, and was employed to treat metal ions in aqueous solutions. The composite hydrogels presented desirable compressive strength and anti-fatigue performance. The adsorption capacities of Cu2+, Pb2+ and Cd2+ reached high values of 193.8, 168.8 and 112.6 mg/g, respectively, in a single-ion adsorption system, due to the plentiful adsorptive sites on the hydrogels. The adsorption kinetics followed the Pseudo-second-order model, while the adsorption isotherm adhered to the Langmuir model. Besides, the hydrogels showed slightly higher selectivity for Cu2+ in an adsorption system involving multiple ions, and exhibited good reusability, enabling it to be employed repeatedly as a recyclable adsorbent. The analysis of the mechanism indicated that ion exchange, electrostatic interaction, chemical adsorption and coordination effect might occurred between metal ions and functional groups in hydrogel matrix and PDA nanopartiles. Moreover, the Cu2+-adsorbed hydrogels could act as a scaffold for the on-site generation of Cu nanoparticles. With the Cu nanoparticles, the hydrogels showed catalytic activity of ∼ 80 % conversion of 4-nitrophenol (1 mmol) to 4-aminophenol in 30 min, validating the dual functional properties for both adsorption and catalytic purposes in the treatment of wastewater.

A robust bi‐level programming approach for optimal planning an off‐grid zero‐energy complex

AbstractElectric power provision for all the customers is not always possible for distribution companies. Some customers are interested in serving their load through renewable resources regarding the climate situation. Electrically off‐grid zero‐energy building is an applicable concept, in which the electrical energy provision of the buildings is isolated from the power supply infrastructures. This paper introduces a robust bi‐level programming model to create a cost‐effective off‐grid zero energy complex in Kish Island under risk management. The upper level of the planning is composed of two components, the passive design of buildings within the complex and the design of a stand‐alone energy system. The passive design as an energy‐saving tool includes the selection of insulation material for building external walls and finding the optimum thickness. Also, the stand‐alone energy system design denotes the sizing of diesel generator, photovoltaic, and battery energy storage as the distributed energy resources. The lower‐level problem optimally handles the annual scheduling of these resources to meet the complex demand under the impact of passive cooling. The Karush–Kuhn–Tucker condition method is used to solve this bi‐level planning problem. Furthermore, the battery degradation is concerned via the throughput model to consider the replacement cost of the problem.

Grid frequency regulation through virtual power plant of integrated energy systems with energy storage

AbstractOwing to the widespread integration of renewable distributed energy resources (DERs), the system frequency stability has been jeopardized by the non‐inertial and stochastic units caused by power electronic devices. The integrated energy system (IES) that combines multi‐vector energy resources can provide energy compensation among sub‐systems in a coordinated fashion to further alleviate the volatility on the electric grid. Under the framework of IES, a virtual power plant (VPP) can aggregate multi‐entities and multi‐vector energy resources to participate in the frequency regulation service while pursuing profit maximization. A three‐stage optimal scheduling model of IES‐VPP that fully considers the cycle life of energy storage systems (ESSs), bidding strategies and revenue settlement has been proposed in this paper under the modified PJM frequency regulation market framework to motivate the aggregated resources to respond to the frequency regulation market actively. The simulation results under various scenarios have verified the feasibility and effectiveness of the proposed model through techno‐economic analysis, the advantages of IES‐VPP have been demonstrated compared with a single vector energy system.

A new voltage sensitivity-based distributed feedback online optimization for voltage control in active distribution networks

Rising sustainability, environmental, and decarbonization considerations have prompted a recent increase in the integration of inverter-based distributed energy resources (DER), such as photovoltaics (PV), into the power grid. This trend raises concerns about potential voltage violations within distribution systems, emphasizing the growing relevance of effective voltage regulation. Utilizing smart inverters for active and reactive power control proves highly efficient in addressing voltage rise issues within active distribution networks (ADN). This work proposes a distributed feedback online optimization (FOO) strategy for voltage regulation in ADNs. The FOO strategy utilizes online recursive least squares (RLS) sensitivity-based feedback, relying on local measurements to iteratively compute optimal reactive power set points for DERs, ensuring bus voltage regulation to within acceptable limits. The distributed control algorithm employs a primal-dual update approach, enabling coordinated information exchange between neighboring controllers. This online feedback-based control offers a promising solution for real-time coordinated voltage control in distribution grids, particularly with the increasing integration of inverter-based DERs at the grid's edge. The approach demonstrates robustness under varying conditions, communication dynamics, and topology changes, validated using real distribution feeders with actual solar PV production and consumption data. Advancements in sensing, communication systems, and smart grid tools further enhance the feasibility and scalability of this strategy for evolving power systems.

Capturing opportunity costs of peer-to-peer energy transactions in microgrids via virtual state-of-charge bids

Since the prosumers can absorb or generate power in the P2P energy market, we equivalently model the prosumers as virtual energy storages (VESs) and employ the latest state-of-charge (SOC)-dependent bids to depict the prosumers’ bidding strategies. We first model the P2P market participants’ trading behaviors considering their loads, batteries, and distributed energy resources (DERs). To derive the equivalent VESs, we develop an algorithm to estimate the prosumers’ virtual capacities. We analyze the market operator’s model to guarantee energy delivery in the distribution network. Then, we project the operator’s network constraints to the prosumers’ feasible regions for privacy preservation and iteration reduction. Based on the virtual capacities, we develop VSOCs to depict the prosumers’ different trading states during market operation. We finally convexify the market-clearing problem and modify the alternating direction method of multipliers (ADMM) to calculate the optimal trading results with VSOC-dependent bids. Two cases are employed to verify that the VSOC-dependent bids can accommodate the prosumers’ parameter changes and trading preferences. The opportunity costs are reduced by 41.2% when allowing prosumers to bid differently in various VSOCs.

Photovoltaic penetration potential in the Greek island of Ikaria

The operation of Distribution Networks (DNs) has been affected by the ongoing energy transition, gradually incorporating more Distributed Energy Resources (DERs), mainly Renewable Energy Sources (RES), as well as Energy Storage Systems (ESS) sustainably enhancing DN’s flexibility. In the case of the non-interconnected island of Ikaria, Greece, with high solar and wind potential, the DN includes conventional generators, Photovoltaic systems (PVs), wind farms and a hydro-pumped ESS. Scope of this study is to assess the possible impact of the PVs expansion considering either: i) fixed, ii) single-axis or iii) dual-axis tracking panels. For this purpose, CERTH’s in-house INTEMA.grid platform is used. Tracking mechanism’s effectiveness is studied considering that the expansion doubles or triples the rated power of the existing, fixed 0.4 MW PVs, following the directions of the Distribution System Operator (DSO). Additionally, a monthly analysis is presented, because Ikaria is an island with extremely higher load during summer months due to tourism. According to the results, if the current PV capacity is doubled or tripled, a dual-axis expansion yields 16.0% or 21.3% yearly production increase compared to fixed panels, respectively, with the single-axis effect though being much higher (14% or 18.7%, respectively) than the incremental effect of the second axis (further comparative 1.8% or 2.3%, respectively). The effectiveness of tracking mechanisms is highlighted during summer months and particularly early in the morning or late in the afternoon. Finally, environmental and economic indicators for the proposed installations are assessed.

Optimal investment of distribution energy resources via energy performance contracts: An evolutionary game approach

Investment in distributed energy resources (DERs) is crucial for the expansion of distribution networks. However, traditional methods often lack comprehensive project management throughout their entire lifecycle and precise matching between distribution network operator (DNO) and energy consumer (EC) at the investment level. To address these issues, this paper proposes a novel investment model based on energy performance contracts (EPCs). Three EPC mechanisms are introduced: energy shared savings model (ESSM), energy guaranteed saving model (EGSM), and energy cost trust model (ECTM). Additionally, DERs investment strategies under EPC mechanisms are proposed to satisfy full-process management of project and precise matching between EC and DNO. The investment and operation problem is solved by an evolutionary game theory that ensures bounded rational agent to achieve satisfactory expected returns through repeated dynamic games. Results from the case study of an actual urban 37-bus system demonstrate that different EPC mechanisms are suitable for various scenarios depending on the EC's profit demand and project scale. The proposed model provides a comprehensive framework for DERs investment, considering the interests of both DNO and EC, and promotes the optimal expansion of distribution networks in a more practical and effective manner.

Comprehensive review of energy management strategies: Considering battery energy storage system and renewable energy sources

AbstractThe transformation of power system networks is slowly taking shape, the advent of interruptive technological platforms dealing with digitalization and real‐time trading of power has gained attention based on incorporation of more renewables into the grid. The stochastic nature of renewables pauses security of supply challenges and other related stability concerns, and for this reason efficient methods are investigated in this review to build an understanding of microgrid energy management system (MG‐EMS) and distribution‐based energy management strategies aimed at transforming the conventional grid network into smart grid network. In essence, propagating a technological shift to microgrids which have proven to be ideal distribution networks for residential and commercial loads, have become indispensable in handling distributed energy resources (DER), such as solar, PV, wind, battery energy storage systems (BESS) and small‐scale microgrids, for example in case of excess supply, energy storage system (ESS) has been formulated as a solution to curb excess supply and can offer ancillary services to the grid network. Within the perspective of electricity generation and distribution, microgrid control methodologies, distribution network (DN) management approaches and incumbent optimization strategies used to coordinate and manage grid‐level uncertainties are investigated. In addition, this study proposes distributionally robust optimization (DRO), to manage and mitigate risks associated to shortage or oversupply of power from RESs.

Performance simulation and analysis of a solar-assisted multifunctional heat pump system for residential buildings

ABSTRACT As building electrification is recognised as an important opportunity to reduce greenhouse gas emissions, the integration of solar energy and heat pump represents a promising solution towards net-zero carbon buildings. This paper presents a hybrid multifunctional solar-assisted heat pump (SAHP) system that can provide space heating, space cooling, domestic hot water, and onsite electricity generation. Photovoltaic-thermal collectors are used for electricity generation, heat collection, and radiative cooling. The system design and controls support fourteen operational modes involving different components. TRNSYS software is used to model and simulate the multifunctional SAHP system. With a 2-m3 storage tank and 30-m2 PVT collectors, the multifunctional SAHP system has a seasonal performance factor of 2.7 in Baltimore and 3.7 in Las Vegas. The onsite electricity generation can cover 53% of the building’s electricity needs in Baltimore and 83% in Las Vegas.

A multi-objective stochastic optimization model for combined heat and power virtual power plant considering carbon recycling and utilizing

In order to give full play to the energy supply potential of distributed energy resources, this paper studies the scheduling optimization of CHP-VPP. First, the CHP unit and various distributed energy sources are aggregated into VPP. Carbon recycling and utilizing are realized through carbon capture and power-to-gas devices. At the same time, carbon storage and hydrogen storage devices are added to decouple carbon capture and P2G procedures. Then, the risk of VPP real-time scheduling is quantified through uncertainty scenario generation and CVaR. Finally, with the goals of operating cost, carbon emission, and operation risk, a multi-objective stochastic scheduling optimization model of VPP is constructed, and the subjective and objective ensemble weighting method is used to solve the problem. The example results show that the proposed method can boost the wastage of wind and photovoltaic power, and also lower the carbon emissions of VPPs.

Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes

IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.

Distributionally robust sequential load restoration of distribution system considering random contingencies

AbstractNatural disasters would destroy power grids and lead to blackouts. To enhance resilience of distribution systems, the sequential load restoration strategy can be adopted to restore outage portions using a sequence of control actions, such as switch on/off, load pickup, distributed energy resource dispatch etc. However, the traditional strategy may be unable to restore the distribution system in extreme weather events due to random sequential contingencies during the restoration process. To address this issue, this paper proposes a distributionally robust sequential load restoration strategy to determine restoration actions. Firstly, a novel multi‐time period and multi‐zone contingency occurrence uncertainty set is constructed to model spatial and temporal nature of sequential line contingencies caused by natural disasters. Then, a distributionally robust load restoration model considering uncertain line contingency probability distribution is formulated to maximize the expected restored load amount with respect to the worst‐case line contingency probability distribution. Case studies were carried out on the modified IEEE 123‐node system. Simulation results show that the proposed distributionally robust sequential load restoration strategy can produce a more resilient load restoration strategy against random sequential contingencies. Moreover, as compared with the conventional robust restoration strategy, the proposed strategy yields a less conservative restoration solution.

Machine learning-based energy management and power forecasting in grid-connected microgrids with multiple distributed energy sources

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Optimal aggregation and disaggregation for coordinated operation of virtual power plant with distribution network operator

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

Large-signal stability analysis of passivity-based droop control of islanded microgrids

In this paper, the large-signal stability properties of a system formed of a single-phase AC Microgrid equipped with a two-level control scheme are analyzed. The aim is to show that it is possible to formally characterize the operative capacities of the controlled system when the nonlinear model that describes its dynamical behavior is considered. Thus, the presented analysis contributes to the formulation of the widely demanded methodology to analyze the large-signal stability properties of Microgrids without invoking small-signal arguments. The analyzed model includes the dynamic of the power converters associated with each distributed energy resource, an inner control law, and a power-sharing mechanism. To better illustrate the scope of the contribution, it is assumed that the Microgrid operates in islanded mode. The inner control schemes are designed using passivity-based ideas, while the power-sharing stage includes the most popular and exhaustively implemented Droop control. The large-signal stability analysis of the Microgrid is carried out by exploiting the Port-controlled Hamiltonian structure of the network and taking advantage of the stability and passivity properties of this class of dynamical systems together with the Input-to-State stability properties of the Droop scheme.

Artificial intelligence driving perception, cognition, decision‐making and deduction in energy systems: State‐of‐the‐art and potential directions

AbstractIn the context of energy systems, managing the complex interplay between diverse power sources and dynamic demands is crucial. With a focus on smart grid technology, continuously innovating artificial intelligence (AI) algorithms, such as deep learning, reinforcement learning, and large language model technologies, have been or have the potential to be leveraged to predict energy consumption patterns, enhance grid operation, and manage distributed energy resources efficiently. These capabilities are essential to meet the requirements of perception, cognition, decision‐making, and deduction in energy systems. Nevertheless, there are some critical challenges in efficiency, interpretability, transferability, stability, economy, and robustness. To overcome these challenges, we propose critical potential directions in future research, including reasonable sample generation, training models with small datasets, enhancing transfer ability, combining with physics models, collective generative pre‐trained transformer‐agents, multiple foundation models, and improving system robustness, to make advancing AI technologies more suitable for practical engineering.

BESS and the ancillary services markets: A symbiosis yet? Impact of market design on performance

The evolution of ancillary services markets (ASM) and balancing products is ongoing. The aim of the evolution is to integrate the products over the national boundaries and to open the ASM to distributed energy resources (DERs). Among DERs, battery energy storage systems (BESS) are increasing their importance. In this work, we investigate by means of numerical simulations the effect of different evolutions in the regulatory framework on the performance of a BESS providing ancillary services. The analyzed regulatory barriers are selected based on ongoing evolution in EU market design. The following parameters are involved: power vs energy-intensive services, symmetry of procurement, time definition and distance to delivery. The considered case study is a BESS associated to a large-scale energy district including load, a cogeneration plant, and a PV plant. Results are given in terms of energy flows, economics, operational efficiency, and reliability of service provision. Where both reliability, provision of large flexibility volumes, and good economic performance are achieved, we say that there is a symbiosis between BESS and the markets. This way, the best ASM arrangements abating regulatory barriers for BESS are defined.

IoT Systems Based Microgrid: A Review

Smart microgrids, as the foundations of the future smart grid, combine distinct Internet of Things (IoT) designs and technologies for applications that are designed to create, regulate, monitor, and protect the microgrid (MG), particularly as the IoT develops and evolves on a daily basis. A smart MG is a small grid that may operate individually or in tandem with the electric grid, and it is ideal for institutional, commercial, and industrial consumers, as well as urban and rural societies. A MG can operate in two methods (stand-alone and grid-connected), with the ability to transition between modes due to local grid faults, planned maintenance, expansions, deficits and failures in the host system, and other factors. Energy storage is the process of storing and converting energy that can be used for a variety of purposes, including voltage and frequency management, power backup, and cost optimization. IoT is designed to deliver solutions for optimal energy management, security protocols, control methods, and applications in the MG, with numerous distributed energy resources (DER) and interconnected loads. The use of IoT architecture for MG operations and controls is discussed in this research. With the use of power grid equipment and IoT-enabled technology