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Abstract A decarbonised future grid should couple technological novelty with innovative market models to efficiently capture the value of grid‐edge decarbonised assets. The transactive energy (TE) concept inverts the centralised grid model by leveraging the evolution of consumers to prosumers to prosumagers. The principal TE market design challenge is transactive control—using market and pricing mechanisms to coordinate autonomous peer interactions, to optimally allocate power and incentivise peers. Peer attraction, incentivisation and retention are all critical for practical TE implementation along three adoption stages, starting from independent peer transactions with the centralised market; to decentralised peer coordination; towards distributed peer‐to‐peer trading. Addressing gaps in related scholarship, the authors investigate the economic positions of distinct peer roles in each adoption stage and two local pricing strategies. Using a real market dataset, trading decisions are simulated over a 1‐year horizon at hourly granularity. Coordinated action achieves better transactive control for the community, with economic superiority over centralised and distributed mechanisms. Distinct peer incentives should equitably align with their contribution to market functionality, such as the value ascribed to prosumagers' flexibility in local pricing and the constrained bargaining power of prosumers in distributed bilateral negotiations.

Abstract Bone marrow stromal/stem cells (BMSCs) are generally considered as common progenitors for both osteoblasts and adipocytes in the bone marrow, but show preferential differentiation into adipocytes rather than osteoblasts under aging, thus leading to senile osteoporosis. Accumulated evidences indicate that rejuvenation of BMSCs by autophagic enhancement delays bone aging. Here we synthetized and demonstrated a novel autophagy activator, CXM102 that could induce autophagy in aged BMSCs, resulting in rejuvenation and preferential differentiation into osteoblasts of BMSCs. Furthermore, CXM102 significantly stimulated bone anabolism, reduced marrow adipocytes, and delayed bone loss in middle-age male mice. Mechanistically, CXM102 promoted transcription factor EB (TFEB) nuclear translocation and favored osteoblasts formation both in vitro and in vivo. Moreover, CXM102 decreased serum levels of inflammation and reduced organ fibrosis, leading to a prolonger lifespan in male mice. Our results indicated that CXM102 could be used as an autophagy inducer to rejuvenate BMSCs and shed new lights on strategies for senile osteoporosis and healthyspan improvement.

The low-carbon agenda promotes the increasing penetration of renewable energy, this however also results in a rapid decrease in the flexibility of power systems with less controllable generation. There is an urgent requirement to explore user-side resources to provide flexibility for the power system. The regional integrated energy system (RIES) contains a large number of multi-energy coupling devices with abundant flexibility. However, considering the multi-energy complementary characteristics and complex multi-energy networks, as well as the uncertainty of the renewable energy, it is difficult to quantify the energy purchasing demand and flexibility support ability of RIES in the market, making it challenging to identify the RIES feasible region. This paper proposes a novel approach to tackle this challenge by introducing a projection method based on second-order cone programming. The approach aims to describe the energy purchasing and flexible reserve feasible regions of an RIES. Specifically, a max–min RIES operating model is constructed with the goal of minimizing the distance from the boundary of the feasible region in the worst case. Subsequently, the strong duality principle and Karush-Kuhn-Tucker (KKT) optimality conditions are leveraged to reformulate the max–min optimization problem into a solvable second-order cone programming problem. Additionally, the chance constraint is introduced to address and express the risk associated the uncertainty of the renewable energy output in RIES. Moreover, for the convenience of subsequent solving, the deterministic equivalent method is used to convert it into a deterministic constraint. Case studies conducted on an RIES incorporating a 33-bus distribution network and a 7-node natural gas network demonstrate the effectiveness and efficiency of the proposed approach in projecting the feasible region in terms of both operating costs and computational time.

A virtual power plant (VPP) refers to an active aggregator of heterogeneous distributed energy resources (DERs), which creates a promising pathway to expand renewable energy and demand-side electrification for deep decarbonization. The VPP market is projected to have a significant growth potential, with the global investment surging from $6.47 billion in 2022 to $16.90 billion by 2030. Up to now, VPPs still face technical challenges in dealing with the inherent uncertainty of DERs, and data emerge as a promising and essential resource to handle this issue. This paper makes the first effort to review the development of VPP technologies from a data-centric perspective, and then analyze the major role of data within every decision phase of VPPs. We examine the VPP energy management through a data lifecycle lens, and extensively survey the progress in data creation, data communication, data-driven decision support, data sharing and privacy, as well as technical solutions stemming from reinforcement learning, peer-to-peer sharing, blockchain, and market participation. In addition, we offer a unique overview of open data and recent real-world projects around the world to showcase the latest VPP practices. We finally discuss the major challenges and future opportunities in detail, with a focus on topics such as public benchmarks, internet of things, 5G, explainable artificial intelligence, and federated learning. We highlight the need for technical advances in data management and support systems for the growing scale of future VPP systems, and suggest VPPs delivering more ancillary grid services in the future.

Bone marrow stromal/stem cells (BMSCs) are primitive and heterogeneous cells that can be differentiated into osteoblasts, adipocytes and other subsets. Their bone-fat lineage commitment is responsible for the homeostasis of bone marrow microenvironment. However, there are little effective methods and evidence to simultaneously visualise the lineage commitment of BMSCs. Here we provide a bivalent differentiation medium that can enable BMSCs differentiation into osteoblasts and adipocytes in vitro, and establish a method to simultaneously distinguish osteoblasts or adipocytes from the heterogeneous BMSCs based on Alizarin red S and Oil red O staining, which have been used for detection of specific mineralized nodules and lipid droplets, respectively. This assay provides a specifically simple but effective and low-cost method to evaluate the efficiency of osteo-adipogenic (OA) allocation of BMSCs. ► Researchers can utilize the bivalent differentiation medium to evaluate the efficiency of osteogenic and adipogenic differentiation of BMSCs in vitro.

This paper proposes a novel bi-level optimization model to study the strategic retail pricing and demand bidding problems of an electricity retailer that considers the interactions between demand response and market clearing process. In order to accurately forecast the day-ahead demand bids submitted by the retailer, a novel deep learning framework based on convolutional neural networks and long short-term memory is proposed that can capture both local trends and long-term dependency of the forecasting data. In addition, uncertainties about the retailer’s served demand, rivals’ demand bids, and wind power generation are incorporated using the data-driven uncertainty set constructed from data. We further propose chance-constrained programming that introduces a set of chance constraints to represent the operational risk associated with the market uncertainties. To solve this problem, we first reformulate chance-constrained programming as a tractable second-order conic programming and then convert it into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker conditions. The scope of the examined case studies is four-fold. First, they evaluate the benefits of the proposed forecasting framework in terms of higher accuracy and expected profit compared to the conventional forecasting methods. Second, they demonstrate how demand flexibility affects the retailer’s strategies and its business cases. Third, they highlight the added value of the proposed bi-level model capturing the market clearing process by comparing its outcomes against the state-of-the-art bi-level model with exogenous market prices. Finally, they analyze the retailer’s strategies and business cases at different confidence levels regarding the imposed chance constraints.

Previously works on analysing imperfect electricity markets have employed conventional game-theoretic approaches. However, such approaches necessitate that each strategic market player has full knowledge of the operating parameters and the strategies of its rivals as well as the computational algorithm of the market clearing process. This unrealistic assumption, along with the modeling and computational complexities, renders such approaches less applicable for conducting practical multi-period and multi-spatial equilibrium analysis. This paper proposes a novel multi-agent deep reinforcement learning (MA-DRL) based methodology, combining multi-agent intelligence, the deep policy gradient (DPG) method, and an innovative long short term memory (LSTM) based representation network for optimizing the offering strategies of multiple self-interested generation companies (GENCOs) as well as exploring the market outcome stemming from their interactions. The proposed approach is tailored to align with the nature of the examined problem by posing it, for the first time, in multi-dimensional continuous state and action spaces, enabling GENCOs to receive accurate feedback regarding the impact of their offering strategies on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, and thereby facilitates more accurate equilibrium analysis. The proposed LSTM-based representation network extracts discriminative features which further improves the learning performance and thus promises more profitable offerings strategies for each GENCO. Case studies demonstrate that the proposed method i) achieves a significantly higher profit than state-of-the-art RL methods for a single GENCO's optimal offering strategy problem and ii) outperforms the state-of-the-art equilibrium programming models in efficiently identifying an imperfect market equilibrium with/without network congestion. Quantitative economic analysis is carried out on the obtained equilibrium.

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

UAV (Unmanned Aerial Vehicle) black flight at low altitude could cause serious safety risks. Consequently, it is crucial to detect and manage low altitude small UAVs. The existing methods of low altitude small UAV detection suffer from problems such as high false alarm rate, and poor real-time performance. In order to solve the above problems, we present a novel approach, named AD-YOLOv5s, to achieve low altitude small UAV detection with high precision and high real-time performance. Firstly, the feature enhancement method is used to expand the dataset. We optimize the model feature fusion, the prediction head structure, and the loss function. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, feature enhancement is performed to improve the detection accuracy. Secondly, the ghost module and depthwise separable convolution are used to reduce the number of parameters of the model, and we propose the method of lightweight design of model to improve the detection speed. Compared with the YOLOv5s model, the experiment result shows that our proposed AD-YOLOv5s model improves the value of mAP by 2.2% and the value of Recall by 1.8%, reduces the value of GFLOPs by 29.9% and parameters by 38.8%, and achieves 27.6 FPS when the proposed model deploy on a low-cost edge computing device (jetson nano). [ABSTRACT FROM AUTHOR]