Phase imbalance in the UK and European low voltage (415V, LV) distribution networks causes additional energy losses.A key barrier against understanding the imbalanceinduced energy losses is the absence of high-resolution time-series data for LV networks. It remains a challenge to estimate imbalance-induced energy losses in LV networks that only have the yearly average currents of the three phases. To address this insufficient data challenge, this paper proposes a new customized statistical approach, named as the CCRE (Clustering, Classification, and Range Estimation) approach. It finds a match between the network with only the yearly average phase currents (the data-scarce network) and a cluster of networks with time series of phase current data (data-rich networks). Then CCRE performs a range estimation of the imbalance-induced energy loss for the cluster of data-rich networks that resemble the data-scarce network. The Chebyshev's inequality is applied to narrow down this range, which represents the confidence interval of the imbalance-induced energy loss for the data-scarce network. Case studies reveal that, given such few data from the data-scarce networks, more than 80% of these networks are classified to the correct clusters and the confidence of the imbalance-induced energy loss estimation is 89%.

Phase unbalance is widespread in the distribution networks in the UK, continental Europe, US, China, and other countries. First, this paper reviews the mass scale of phase unbalance and its causes and consequences. Three challenges arise from phase rebalancing: the scalability, data scarcity, and adaptability (towards changing unbalance over time) challenges. Solutions to address the challenges are: 1) using retrofit-able, maintenance-free, automatic solutions to overcome the scalability challenge; 2) using data analytics to overcome the data-scarcity challenge; and 3) using phase balancers or other online phase rebalancing solutions to overcome the adaptability challenge. This paper categorizes existing phase rebalancing solutions into three classes: 1) load/lateral re-phasing; 2) using phase balancers; 3) controlling energy storage, electric vehicles, distributed generation, and micro-grids for phase rebalancing. Their advantages and limitations are analyzed and ways to overcome the limitations are recommended. Finally, this paper suggests future research topics: 1) long-term forecast of phase unbalance; 2) whole-system analysis of the unbalance-induced costs; 3) phase unbalance diagnosis for data-scarce LV networks; 4) technocommercial solutions to exploit the flexibility from large threephase customers for phase balancing; 5) the optimal placement of phase balancers; 6) the transition from single-phase customers to three-phase customers.

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The long-term uncertainty of multi-energy demand poses significant challenges to the coordinated pricing of multiple energy systems (MES). This paper proposes an integrated network pricing methodology for MES based on the long-run-incremental cost (LRIC) to recover network investment costs, affecting the siting and sizing of future distributed energy resources (DERs) and incentivizing the efficient utilization of MES. The stochasticity of multi-energy demand growth is captured by the Geometric Brownian Motion (GBM)-based model. Then, it is integrated with a system operation model to minimize operation costs, considering low-carbon targets and flexible demand. Thereafter, the kernel density estimation (KDE) method is used to perform the probabilistic optimal energy flow (POEF) to obtain energy flows under uncertain load conditions. Based on the probability density functions (PDFs) of energy flows, an LRIC-based network pricing model is designed, where Tail Value at Risk (TVaR) is used to model the risks of loading levels of branches and pipelines. The performance of the proposed methodology is validated on a typical MES. The proposed pricing method can stimulate cost-effective planning and utilization of MES infrastructures under long-term uncertainty, thus helping reduce low-carbon transition costs.

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Phase unbalance is widespread in the distribution networks in the UK, continental Europe, US, China, and other countries. First, this paper reviews the mass scale of phase unbalance and its causes and consequences. Three challenges arise from phase rebalancing: the scalability, data scarcity, and adaptability (towards changing unbalance over time) challenges. Solutions to address the challenges are: 1) using retrofit-able, maintenance-free, automatic solutions to overcome the scalability challenge; 2) using data analytics to overcome the data-scarcity challenge; and 3) using phase balancers or other online phase rebalancing solutions to overcome the adaptability challenge. This paper categorizes existing phase rebalancing solutions into three classes: 1) load/lateral re-phasing; 2) using phase balancers; 3) controlling energy storage, electric vehicles, distributed generation, and micro-grids for phase rebalancing. Their advantages and limitations are analyzed and ways to overcome the limitations are recommended. Finally, this paper suggests future research topics: 1) long-term forecast of phase unbalance; 2) whole-system analysis of the unbalance-induced costs; 3) phase unbalance diagnosis for data-scarce LV networks; 4) techno-commercial solutions to exploit the flexibility from large three-phase customers for phase balancing; 5) the optimal placement of phase balancers; 6) the transition from single-phase customers to three-phase customers. <br>