Distributed management of energy communities using stochastic profile steering

This paper presents a novel approach for distributed management of energy communities. The proposed method utilizes a stochastic profile steering algorithm as a greedy heuristic. The optimization process considers random parameters such as local forecasted demand, photovoltaic (PV) production, and the initial energy of electric vehicles (EVs) as embedded scenarios. Profile steering coordinates the flexible electricity assets within an energy community by determining the contribution of each prosumer's profile to the average value of the objective function. It iteratively selects the prosumer that contributes the most until no further improvements can be made. This process scales linearly with the number of controllable prosumers and can achieve various community-level objectives, such as maximizing self-sufficiency or minimizing aggregated cost-of-energy, even when dealing with non-convex optimization problems for modeling each prosumer's local energy management system. The outcome of the proposed method optimizes the average value of the community's objective while ensuring that grid limitations are met within a specified probability. The proposed method is evaluated through simulations involving small-scale communities (5 households) and large-scale communities (100 households). The results demonstrate the efficiency, flexibility, and scalability of the proposed method, as well as its ability to reschedule the aggregated demand to ensure that grid limits are not violated with at least a 95% probability.</p

Distributed Co-operative Demand Side Management for Energy Communities

This paper presents a method to implement cooperative Demand Side Management (DSM) approaches over a distributed communication network. Our method combines a decentralized DSM approach called Profile Steering with two distributed consensus mechanisms Proof of Work (PoW) and Proof of Stake (PoS). We make use of a network manager to moderate the network and a distributed ledger system to store the power profiles from each planning period.We evaluate our approach using publicly-available real data from 25 houses. We successfully implemented our distributed approach in Python using JSON files as blocks and performed time-performance evaluations of this implementation. We show that, distributed DSM for day-ahead planning using blockchain concepts is possible and adds an overhead of only 0.98 seconds and 3.99 seconds in the case of PoW and PoS respectively

Modeling and efficiency enhancement of SnSe thin film solar cell with a thin CIS layer

Research has been conducted on a solar cell utilizing Tin Selenide (SnSe) as the absorber material having a structure of n-CdS/p-SnSe/p+-CuInSe2/p++-WSe2 with CuInSe2 (CIS) current augmenting and WSe2 back surface field (BSF) layers. The various parameters in CdS, SnSe, CIS, and WSe2 layers have been adjusted methodically for the optimum output. Under the optimum conditions, the proposed n-CdS/p-SnSe solar cell showcases a power conversion efficiency (PCE) of 26.12 % with VOC = 0.788 V, JSC = 38.62 mA/cm2, and FF = 85.78 %. The PCE of the device enhances to 33.88 % with VOC = 1.09 V, JSC = 38.64 mA/cm2, and FF = 80.24 % owing to the incorporation of the WSe2 BSF layer. However, a conjunction of the CIS layer with the SnSe achieves a higher JSC of 42.54 mA/cm2 and a PCE of 36.45 %. These in-depth simulation findings demonstrate the enormous potential of the SnSe absorber with the CIS current boosting layer for the creation of a thin film solar cell that is both affordable and highly efficient

On the Effects of Active Energy Community Participation in the Energy System

This paper explores the effects of active energy communities, as defined by EU Directive 2019/944, on the energy system using simulations modelled based on a real-world ecological community. The energy flexibility of this community is optimized towards the objectives peak reduction, cost minimization, and CO2 emission minimization. The resulting effects for different stakeholders, such as network operators and the community itself, are investigated using different performance indicators. The results show that energy imports can be reduced by 44% when peak reduction is applied. Conflicting objectives may lead to peak synchronization however, with the risk of deteriorating business cases if the network operator needs to intervene. This results in a risk where energy communities abstain from participation in energy market and its benefits

Improved Atomistic Monte Carlo Simulations Demonstrate That Poly-l-Proline Adopts Heterogeneous Ensembles of Conformations of Semi-Rigid Segments Interrupted by Kinks

Poly-l-proline (PLP) polymers are useful mimics of biologically relevant proline-rich sequences. Biophysical and computational studies of PLP polymers in aqueous solutions are challenging because of the diversity of length scales and the slow time scales for conformational conversions. We describe an atomistic simulation approach that combines an improved ABSINTH implicit solvation model, with conformational sampling based on standard and novel Metropolis Monte Carlo moves. Refinements to forcefield parameters were guided by published experimental data for proline-rich systems. We assessed the validity of our simulation results through quantitative comparisons to experimental data that were not used in refining the forcefield parameters. Our analysis shows that PLP polymers form heterogeneous ensembles of conformations characterized by semirigid, rod-like segments interrupted by kinks, which result from a combination of internal <i>cis</i> peptide bonds, flexible backbone ψ angles, and the coupling between ring puckering and backbone degrees of freedom

Improved atomistic Monte Carlo simulations demonstrate that poly-L-proline adopts heterogeneous ensembles of conformations of semi-rigid segments interrupted by kinks

Poly-L-proline (PLP) polymers are useful mimics of biologically relevant proline-rich sequences. Biophysical and computational studies of PLP polymers in aqueous solutions are challenging because of the diversity of length scales and the slow time scales for conformational conversions. We describe an atomistic simulation approach that combines an improved ABSINTH implicit solvation model, with conformational sampling based on standard and novel Metropolis Monte Carlo moves. Refinements to forcefield parameters were guided by published experimental data for proline-rich systems. We assessed the validity of our simulation results through quantitative comparisons to experimental data that were not used in refining the forcefield parameters. Our analysis shows that PLP polymers form heterogeneous ensembles of conformations characterized by semirigid, rod-like segments interrupted by kinks, which result from a combination of internal cis peptide bonds, flexible backbone ψ angles, and the coupling between ring puckering and backbone degrees of freedom