Search Results (9)

The effective management of energy interactions in multi-island microgrid systems presents a significant challenge due to the geographical dispersion of islands. To address this, this paper proposes a hierarchical distributed optimization strategy based on the alternating direction method of multipliers (ADMM). The strategy features a two-layer architecture: the upper layer employs the ADMM to solve the system-level optimal power flow problem and generates distributed node marginal electricity prices (DLMPs) as clear economic coordination signals. The lower layer consists of individual island microgrids, which independently and in parallel solve their internal security-constrained economic dispatch (SCED) problems upon receiving the converged DLMP signals. This layered decoupling design functionally separates system-level coordination from microgrid-level optimization and enhances privacy protection by preventing the exposure of internal cost functions and operational constraints during upper-layer iterations. Case studies demonstrate that the proposed strategy reduces total operating costs by 10.3% compared to a centralized approach, while also significantly decreasing communication data volume by 83% and ensuring robust privacy protection. The algorithm exhibits good scalability with sublinear growth in iteration counts as the system scales, validating its effectiveness and practical potential for enhancing energy management in multi-island microgrid systems. Full article

Constrained by current market mechanisms, small-scale virtual power plants (SS-VPPs) on the distribution network side struggle to exert their market characteristics. To address this, this paper proposes a trading framework and operational strategy for distribution-side SS-VPPs to participate in the day-ahead energy market. First, an operation and trading framework for distribution networks involving SS-VPPs is proposed. This framework comprehensively considers the clearing process of the electricity energy market, the operation mechanism of the distribution network, and the cost structures of various stakeholders, while clarifying the day-ahead market clearing mechanism at the distribution network level. Next, accounting for energy balance constraints and distribution network congestion constraints, this paper establishes a collaborative optimization model between SS-VPPs and active distribution networks. After obtaining the energy optimization results for all stakeholders, distribution locational marginal pricing (DLMP) is determined based on the dual problem solution to achieve multi-stakeholder market clearing. Finally, simulations using a modified IEEE 33-node test system demonstrate the rationality and feasibility of the proposed strategy. The framework fully exploits the market characteristics and dispatch potential of SS-VPPs, significantly reduces overall system operating costs, and ensures the economic benefits of all participants. Full article

This paper seeks to provide a cost benefit analysis of the implementation of a vehicle-to-grid (V2G) charging strategy relative to a smart charging (V1G) strategy from the perspective of an individual electric vehicle (EV) owner with and without solar photovoltaics (PV) located on their roof. This work utilizes a novel AC optimized power flow model (ACOPF) to produce distributed location marginal prices (DLMP) on a modified IEEE-33 node network and uses a complete set of real-world costs and benefits to perform this analysis. Costs, in the form of the addition of a bi-directional charger and the increased vehicle depreciation incurred by a V2G strategy, are calculated using modern reference sources. This produces a more true-to-life comparison of the V1G and V2G strategies from the frame of reference of EV owners, rather than system operators, with parameterization of EV penetration levels performed to look at how the choice of strategy may change over time. Counter to much of the existing literature, when the analysis is performed in this manner it is found that the benefits of implementing a V2G strategy in the U.S.—given current compensation schemes—do not outweigh the incurred costs to the vehicle owner. This result helps explain the gap in findings between the existing literature—which typically finds that a V2G strategy should be favored—and the real world, where V2G is rarely employed by EV owners. Full article

Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation. Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists. Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min). Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight. Full article

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method. Full article

With the development of the new energy vehicle market, the pricing of battery swapping stations (BSS) is becoming a concern. The pricing models of BSS usually only consider the interaction between the distribution system operator (DSO) and the BSS or between the BSS and electric vehicles (EVs). The impact of DSO and EVs on the pricing strategy of BSS has received less attention, which does not reflect the actual complex situation. Therefore, we propose a three-level BSS pricing method that includes market clearing and EV behaviors. Firstly, the distribution locational marginal price (DLMP) is modeled to determine the impact of the DSO on BSS. Secondly, the EV demand response is used to estimate the impact of EVs on BSS. Thirdly, to increase the adaptability of this model, an iteration algorithm with approximations and relaxations is used with mixed integer linear programming, effectively solving the pricing optimization. According to this optimization, it is evident that the BSS make decisions in the market environment by monitoring the quantity of batteries in various states and generate extra income by acting in response to price fluctuations in the electricity market. The model’s viability and applicability are confirmed. Full article

This work proposes a distribution locational marginal price (DLMP)-based transactive energy (TE) framework for distribution systems with enthusiastic or smart prosumers. The framework uses a multi-agent system (MAS) as the basis on which the proposed TE model, i.e., distribution locational marginal price (DLMP) based TE management system (DTEMS), is implemented. DTEMS uses a novel metric known as the nodal earning component, which is determined by the optimal power flow (OPF) based smart auction mechanism, to schedule the TE transactions optimally among the stakeholders by alleviating the congestion in the distribution system. Based on the individual contributions to the congestion relief, DTEMS ranks the prosumers and loads as most valuable players (MVP) and assigns the energy trading price according to the category of the player. The effectiveness of the proposed TE model is verified by simulating the proposed DTEMS for a modified 33 bus radial distribution system fed with various plug-able energy resources, prosumers, and microgrids. Full article

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users. Full article

In a transactive energy market, distributed energy resources (DERs) such as dispatchable distributed generators (DGs), electrical energy storages (EESs), distribution-scale load aggregators (LAs), and renewable energy sources (RESs) have to earn their share of supply or demand through a bidding process. In such a market, the distribution system operator (DSO) may optimally schedule these resources, first in a forward market, i.e., day-ahead, and in a real-time market later on, while maintaining a reliable and economic distribution grid. In this paper, an efficient day-ahead scheduling of these resources, in the presence of interaction with wholesale market at the locational marginal price (LMP), is studied. Due to inclusion of EES units with integer constraints, a detailed mixed integer linear programming (MILP) formulation that incorporates simplified DistFlow equations to account for grid constraints is proposed. Convex quadratic line and transformer apparent power flow constraints have been linearized using an outer approximation. The proposed model schedules DERs based on distribution locational marginal price (DLMP), which is obtained as the Lagrange multiplier of the real power balance constraint at each distribution bus while maintaining physical grid constraints such as line limits, transformer limits, and bus voltage magnitudes. Case studies are performed on a modified IEEE 13-bus system with high DER penetration. Simulation results show the validity and efficiency of the proposed model. Full article