Optimal sizing for microgrids integrating distributed flexibility with the Perth West smart city as a case study

With the decreasing cost of green technologies and the increasing ambitions to reach the net-zero carbon emissions target, more communities are engaged in renewable deployment and energy-intensive technologies such as heat pumps and electric vehicles will be intensively adopted in the near future. The integration of these appliances in lower grid levels will likely require grid reinforcements. However, some of these appliances are flexible and there is an opportunity to explore their flexibility potential to optimise the investment costs further. This paper proposes an optimal design strategy for a grid-connected site that returns the renewable generation and storage's optimal sizing capacities and the required network reinforcement capacity. The novelty of the work is integrating network upgrade costs and considering flexibility from distributed flexible resources across planning and operation. The problem is formulated as a mixed integer piecewise linear problem, with the capacities of generation, storage and network upgrade as decision variables. The piecewise linear cost function related to the upgrade costs figuring in the objective function is then recast as a mixed-integer problem, and the flexible resources are modelled through an approximation method as a single virtual flexible asset. The application of the strategy on the Perth West smart city project as a case study demonstrates the importance of considering flexibility in the planning phase. The costs related to the storage system can decrease by up to 76%, and the overall costs by up to 35%, with the highest levels of savings, reached for the highest rates of electric vehicle adoption

Aggregated feasible active power region for distributed energy resources with a distributionally robust joint probabilistic guarantee

Distributed Energy Resources (DERs) have valuable flexibility to provide grid services. The Aggregated Feasible Active Power Region (AFAPR) is useful for aggregating DERs and reducing the computational burden in system-wide DER scheduling. However, the uncertainty of DERs calls for a reliable AFAPR. This paper proposes a novel surrogate polytope method for deriving the inner approximation of the AFAPR that is jointly reliable for all DER constraints and linear network constraints across the scheduling period. Instead of directly applying the chance constraints to the low-level DER constraints and network constraints, the proposed method applies the Wasserstein Distributionally Robust Joint Chance Constraint (WDRJCC) to the surrogate polytope approximation of the AFAPR, which is reformulated into a tractable set of Mixed Integer Linear Programming (MILP) constraints. Our derived inner approximation to the reliable AFAPR is less conservative while still being reliable, as demonstrated by comparisons with four benchmarks in extensive case studies, and with the nonlinear Z-Bus power flow simulation applied to validate the satisfaction of network constraints. The historical data size required is small, making the proposed method easier to deploy. The scale of MILP constraints is small and does not increase with the network size nor with the number of DERs

Datasets of Great Britain primary substations integrated with household heating information

The growing demand for electrified heating, electrified transportation, and power-intensive data centres challenge distribution networks. If electrification projects are carried out without considering electrical distribution infrastructure, there could be unexpected blackouts and financial losses. Datasets containing real-world distribution network information are required to address this. However, the existing dataset at NERC that covers the whole of Great Britain (GB) does not provide information about demand and capacity, which is insufficient for evaluating the connection feasibility. Although each distribution network operator (DNO) has detailed network information for their supply area, the information is scattered in separate files and different formats even within the same DNO, which limits usability. On the other hand, studying the coupling between energy systems and societal attributes such as household heating is important in promoting social welfare, which calls for more comprehensive datasets that integrate the social data and the energy network data. However, social datasets are usually provided on a regional basis, and the link to energy networks is not straightforward, which explains the lack of the comprehensive datasets. To fill these gaps, this paper introduces two datasets. The first is the main dataset for the GB distribution networks, collecting information on firm capacity, peak demands, locations, and parent transmission nodes (grid supply points, namely GSPs) for all primary substations (PSs). PSs are a crucial part of UK distribution networks and are at the lowest voltage level (11 kV) with publicly available data. Substation firm capacity and peak demand facilitate an understanding of the remaining room in the existing network. The parent GSP information helps link the released datasets to transmission networks. These datasets are collected, standardised, and merged from various files with different formats published by the six DNOs in GB, using a Python script and manual validation. The second dataset extends the main network dataset, linking each PS to the number of households that use different types of central heating recorded in census data (Census in year 2021 for England and Wales, and Census 2011 for Scotland as the up-to-date Census 2022 data is not fully released). The derivation of the second dataset is based on the locations of PSs collected in the main dataset with appropriate assumptions. The derivation process may be replicated to integrate other social datasets. The datasets have the following reuse potentials: 1) Given the PS demand, capacity, and locations in our datasets, users can estimate the connection feasibility and evaluate the optimal deployment locations for different energy technologies, including electric vehicles, heat pumps, and the growing data centres, under different scenarios and at a national scale. These evaluations are beneficial not only for academic research, but also for industrial planning and policy making. 2) Our extended dataset links household information to distribution networks. The integrated information facilitates cross-disciplinary research and analysis across social science, energy policy, and power systems. 3) The network demand and capacity information provided by the datasets can also help with realistic parameter settings to improve the accuracy of case studies in broader power system research

Evaluating the social benefits and network costs of heat pumps as an energy crisis intervention

Fuel poverty, a pressing issue affecting social prosperity, has been exacerbated during the energy crisis triggered by the Russia-Ukraine conflict. This problem can be more severe for off-gas regions. Our study investigates heat pumps (HPs) as a cost-effective alternative to off-gas heating to alleviate fuel poverty in England and Scotland. We analyze regional fuel poverty rates and the associated greenhouse gas emission reduction by replacing all off-gas heating with HPs, observing positive effects under pre-crisis and crisis conditions, with existing government support for HP upfront costs. HP rollout can burden distribution networks especially for certain regions, but our correlation analysis shows that high benefits do not always come with network costs at the regional level, and we identify "priority" regions with low costs and high benefits. These findings provide valuable insights for policymakers to address fuel poverty and reach decarbonization. The methodology is adaptable to other countries with appropriate datasets

Stacking Revenues from Flexible DERs in Multi-Scale Markets using Tri-Level Optimization

Rapid proliferation of flexible Distributed Energy Resources (DERs) as a result of Net Zero Emissions objectives entails a profound shift in the paradigm of local and national energy systems. Currently, DERs' simultaneous participation in multiple markets is generally restricted, which undermines their profitability. With the aim of increasing the number of business cases for them, a tri-level optimization problem that seeks the maximisation of revenues from DERs is proposed. The optimization problem considers simultaneous participation of different flexible DERs, such as, Electric Vehicles (EVs), Battery Energy Storage Systems (BESSs) and Heating, Ventilation and Air Conditioning (HVACs), in national and local markets. Markets are cleared sequentially, and the model is recast into a tractable single-level problem using its dual formulation and strong duality condition. Results from a case study based on the IEEE 14 bus transmission network, a realistic distribution network and SimBench dataset demonstrate the effectiveness of the proposed approach in increasing profits compared with a baseline scenario

Cost-Effective Energy Usage in a Microgrid Using a Learning Algorithm

The microgrid is a new concept of integrating the distributed energy resources (DER) within the grid. The management of the heterogeneous sources of energy presents a challenge, especially as most of the DER are unpredictable. Besides, implementing microgrids should be economically beneficial to the customer; this will raise the challenge of decreasing the costs while ensuring the energy balance. In this paper, we used a stochastic approach based on a model-free Markov decision process (MDP) to derive the optimal strategy for the home energy management system. The approach aims to decrease the energy bill while taking into account the intermittency of the renewable energy resources (DER) and other constraints. While other proposals charge the battery from the utility energy, making the state of charge (SOC) of the battery a deterministic variable, our work adopts a scenario where the battery is charged from the excess of the generated energy, which makes the SOC a nondeterministic variable affected by the uncertain character of the renewable energy. Therefore, our model considers the randomness at two levels: renewable energy level and battery SOC level. We take into account the complexity of the solution, and we propose a simple strategy that can be implemented easily in microgrids

Evaluating the social benefits and network costs of heat pumps as an energy crisis intervention

Summary: Fuel poverty, a pressing issue affecting social prosperity, has been exacerbated during the energy crisis triggered by the Russia-Ukraine conflict. This problem can be more severe for off-gas regions. Our study investigates heat pumps (HPs) as a cost-effective alternative to off-gas heating to alleviate fuel poverty in England and Scotland. We analyze regional fuel poverty rates and the associated greenhouse gas emission reduction by replacing all off-gas heating with HPs, observing positive effects under pre-crisis and crisis conditions, with existing government support for HP upfront costs. HP rollout can burden distribution networks especially for certain regions, but our correlation analysis shows that high benefits do not always come with network costs at the regional level, and we identify “priority” regions with low costs and high benefits. These findings provide valuable insights for policymakers to address fuel poverty and reach decarbonization. The methodology is adaptable to other countries with appropriate datasets