Frequency-constrained energy and reserve scheduling in wind incorporated low-inertia power systems considering vanadium flow redox batteries

This paper proposes a novel energy and reserve scheduling model for power systems with high penetration of wind turbines (WTs). The objective of the proposed model is to minimize the total operation cost of the system while static and dynamic security is guaranteed by preserving the frequency nadir, RoCoF, and quasi-steady-state frequency in the predefined range. Likewise, a supervisory, control, and data acquisition (SCADA) system is developed which allows Vanadium Redox Flow Batteries (VRFBs) to continuously communicate and participate in the primary frequency response. To cope with the uncertainties, adaptive information gap decision theory is used that ensures a target operating cost for the risk-averse operator of the power system. The proposed scheduling model is applied on a modified IEEE 39 bus test system to verify the impacts of the fast reserve provided by the VRFBs in the dynamic frequency security enhancement of the power system with high penetration of WTs

Effects of Calendar and Cycle Ageing on Battery Scheduling for Optimal Energy Management: A Case Study of HSB Living Lab

This paper deals with the optimal scheduling of abuilding microgrid coupled with solar photovoltaic and batteryenergy storage (BES) considering battery degradation. The aim isto minimize the operation cost of the microgrid which includes thecost of imported electricity from the grid, the degradation cost ofthe battery, the cost for the peak power drawn from the grid, andthe revenue from selling electricity to the grid. The nonlinearmodels of calendar and cycle ageing are linearized to solve theoptimal scheduling as a mixed-integer linear programming(MILP) problem. The developed model is examined for a realresidential building microgrid (HSB Living Lab) in Gothenburg,Sweden. The results show that if the degradation of the BES isignored, the operation cost of the microgrid will increase by 1,394SEK per year, and the ageing cost of the BES will also rise by42.27%

Curcumin increases insulin sensitivity in C2C12 muscle cells via AKT and AMPK signaling pathways

Background: Diabetes is an enduring condition that causes impaired, peripheral insulin resistance. Curcumin was shown to exert notable anti-diabetic effects, which might be possible by overexpression of certain glucose transporter genes and glycoprotein content within the cells. Objectives: To investigate the effect of curcumin alone and in concomitant with insulin on glucose translocation from intracellular compartments of nuclear or endoplasmic reticulum membranes into the cytoplasmic membrane (CM) and key kinases involved in insulin signaling pathways. Materials and Methods: C2C12 myoblast cells were cultured and differentiated to the myotubes. Later, the cells were treated with curcumin alone or a combination of curcumin and insulin, so their viability was measured by MTT assay. The expression level of GLUT 4 gene was examined by Real Time-reverse Transcription Polymerase Chain Reaction (qRT-PCR). To evaluate the activity of curcumin and curcumin/insulin synergistic effect on stimulated GLUT4 translocation, kinases AKT, P-AKT, AMP-activated protein kinase (AMPK) and P-AMPK were assessed and detected via Western Blotting (WB). Results: Curcumin significantly induced GLUT 4 expression and its translocation from intra-cell space into the cell surface and showed a synergic effect on GLUT 4 translocation in presence of insulin. This synergistic effect was inhibited by the insulin receptor inhibitor AG1024 and the inhibitor of AMPK signaling, compound C. Conclusion: Curcumin demonstrated a synergistic effect with insulin and could be a choice of type 2 diabetes mellitus (T2DM) treatment, which may be affected by both AKT and AMPK signaling pathways, hereby facilitates glucose uptake into the cells

A Decentralized Transactive-Based Model for Reactive Power Ancillary Service Provision by Local Energy Communities

Local Energy Communities (LECs) have been introduced to facilitate the high penetration of distributed energy resources into distribution systems (DSs), which can also be valuable sources of ancillary services for Distribution System Operators (DSOs). The aim of this paper is to investigate if and how LECs can contribute to the reactive power management of DSs. To this end, a transactive-based model is proposed to manage the reactive power of LECs in a decentralized way. In the proposed model, DSO sends transactive signals to LECs and incentivizes them to inject/absorb reactive power aiming at controlling the voltage profile of DS. Accordingly, the reactive power management and thus voltage control is performed without any direct interferences of the DSO in the resource scheduling of LECs. The proposed model is applied to the DS of Chalmers campus to demonstrate its performance in reactive power management and voltage control

Hardware Implementation New Zero-Setting Power Swing Detection and Fast Detection Symmetrical Fault during Power Swing Algorithms

Distance relays, which measure the positive impedance, may mal-operate due to power swings. For maintaining security, the power swing blocking (PSB) function is embedded in commercial distance relays to avoid unintended operation during power swing. However, distance relays must operate if a fault occurs during the power swing. Most commercial relays focus on the concentrate characteristics method for detecting the power swing. The main disadvantages of this method are pre-dynamic studies which are not possible in some grids. For detecting fault during power swing, commercial relays mostly monitor the zero sequence of the current waveform, which is not noticeable in three-phase faults. In this paper, a new zero-setting PSD method will be proposed, which not need any pre-studies. By considering the transmission line as series resistance and reactance, with local data, the difference voltage angle between sending and receiving-end terminals (Δδ) will be calculated. Also, an algorithm for the prediction of fault during power swing based on the predicted Δδ is studied. Thus, a new method is proposed based on comparing the calculated and prediction value of the difference voltage angle between sending and receiving-end terminals. The proposed method is also implemented in a Hardware-In-the-Loop (HIL) setup and its performance is evaluated

A Risk-Averse Energy Management System for Optimal Heat and Power Scheduling in Local Energy Communities

Local energy communities (LECs) facilitate energy distribution, supply, consumption, storage, and trading for the communities and their members. This paper proposes a risk-averse energy management system (EMS) for optimal heat and power scheduling in LECs. Three approaches namely high accuracy forecast models, advanced optimization models, and providing flexibility sources are followed to handle uncertainties of photovoltaic power and load. To this end, the load demand and photovoltaic power as uncertain variables are predicted using machine learning methods and the problem is modeled under uncertainties by information-gap decision theory (IGDT). This method doesn\u27t require probability distribution functions of uncertain variables which makes it valuable in cases with high levels of uncertainties or lack of sufficient historical data. The advantage of flexibility in increasing robustness is studied by adjusting desired indoor and hot water temperatures. The effectiveness and efficiency of the proposed model are evaluated on the LEC at Chalmers University of Technology campus, Gothenburg, Sweden

FlexiGrid Tools for Real-Life Demonstrations of Local Energy System Concepts at Chalmers Campus Testbed

The ongoing transition in the energy system is accompanied by various challenges such as congestion and voltage violations in electricity distribution networks. Suggested solutions to these challenges are, among others, local energy and flexibility markets, new tariff designs, and centralized control. Simulation and demonstration of such solutions often require a broad chain of tools that need to be automated. The aim of this paper is to present a simulation and demonstration platform integrating several of the required tool and their deployment at the Chalmers Campus test bed. The developed tools include PV and load forecasting, congestion forecasting, energy management system, model predictive control algorithms, and local energy and flexibility markets. The tools are integrated using a common and reusable platform called LESOOP for simulation and demonstration. Demonstration examples of PV and load forecasts, congestion forecasts, model predictive control, and energy management systems are presented and results are discussed. The paper contributes to a more successful transition of the energy system by sharing experiences and provides better understanding of the tool functionalities at the Chalmers campus test-bed. The identified gaps and gained knowledge from the real-life demonstrations at the test-bed can be valuable to projects and researchers aiming to develop a test-bed