Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

Construction, analysis, ligation, and self-assembly of DNA triple crossover complexes

This paper extends the study and prototyping of unusual DNA motifs, unknown in nature, but founded on principles derived from biological structures. Artificially designed DNA complexes show promise as building blocks for the construction of useful nanoscale structures, devices, and computers. The DNA triple crossover (TX) complex described here extends the set of experimentally characterized building blocks. It consists of four oligonucleotides hybridized to form three double-stranded DNA helices lying in a plane and linked by strand exchange at four immobile crossover points. The topology selected for this TX molecule allows for the presence of reporter strands along the molecular diagonal that can be used to relate the inputs and outputs of DNA-based computation. Nucleotide sequence design for the synthetic strands was assisted by the application of algorithms that minimize possible alternative base-pairing structures. Synthetic oligonucleotides were purified, stoichiometric mixtures were annealed by slow cooling, and the resulting DNA structures were analyzed by nondenaturing gel electrophoresis and heat-induced unfolding. Ferguson analysis and hydroxyl radical autofootprinting provide strong evidence for the assembly of the strands to the target TX structure. Ligation of reporter strands has been demonstrated with this motif, as well as the self-assembly of hydrogen-bonded two-dimensional crystals in two different arrangements. Future applications of TX units include the construction of larger structures from multiple TX units, and DNA-based computation. In addition to the presence of reporter strands, potential advantages of TX units over other DNA structures include space for gaps in molecular arrays, larger spatial displacements in nanodevices, and the incorporation of well-structured out-of-plane components in two-dimensional arrays

Dynamic pricing for responsive demand to increase distribution network efficiency

This paper designs a novel dynamic tariff scheme for demand response (DR) by considering networks costs through balancing the trade-off between network investment costs and congestion costs. The objective is to actively engage customers in network planning and operation for reducing network costs and finally their electricity bills. System congestion costs are quantified according to generation and load curtailment by assessing their contribution to network congestion. Plus, network investment cost is quantified through examining the needed investment for resolving system congestion. Customers located at various might face the same energy signals but they are differentiated by network cost signals. Once customers conduct DR during system congested periods, the smaller savings from investment and congestion cost are considered as the economic singles for rewarding the response. The innovation is that the method translates network congestion/investment costs into tariffs, where current research is mainly focused on linking customer response to energy prices. A typical UK distribution network is utilised to illustrate the new approach and results show that derived economic signals can effectively benefit end customers for reducing system congestion costs and deferring required network investment.</p

Novel cost model for balancing wind power forecasting uncertainty

The intermittency of wind generation creates nonlinear uncertainties in wind power forecasting (WPF). Thus, additional operating costs can be incurred for balancing these forecasting deviations. Normally, large wind power penetration requires accurate quantification of the uncertainty-induced costs. This paper defines this type of costs as wind power uncertainty incremental cost (WPUIC) and wind power uncertainty dispatch cost (WPUDC), and it then formulates a general methodology for deriving them based on probabilistic forecasting of wind power. WPUIC quantifies the incremental cost induced from balancing the uncertainties of wind power generation. WPUDC is a balancing cost function with a quadratic form considering diverse external conditions. Besides, the risk probability (RP) of not meeting the scheduled obligation is also modelled. Above models are established based on a newly developed probabilistic forecasting model, varying variance relevance vector machine (VVRVM). Demonstration results show that the VVRVM and RP provide accurate representation of WPF uncertainties and corresponding risk, and thus they can better support and validate the modelling of WPUDC and WPUIC. The proposed cost models have the potential to easily extend traditional dispatches to a new low-carbon system with a high penetration of renewables.</p

Robust Optimization-Based Energy Storage Operation for System Congestion Management

Power system operation faces an increasing level of uncertainties from renewable generation and demand, which may cause large-scale congestion under an ineffective operation. This article applies energy storage (ES) to reduce system peak and the congestion by the robust optimization, considering the uncertainties from the ES state-of-charge (SoC), flexible load, and renewable energy. First, a deterministic operation model for the ES, as a benchmark, is designed to reduce the variance of the branch power flow based on the least-squares concept. Then, a robust model is built to optimize the ES operation with the uncertainties in the severest case from the load, renewable energy, and ES SoC that are converted into branch flow budgeted uncertainty sets by the cumulant and Gram–Charlier expansion methods. The ES SoC uncertainty is modeled as an interval uncertainty set in the robust model, solved by the duality theory. These models are demonstrated on a grid supply point to illustrate the effectiveness of a congestion management technique. Results illustrate that the proposed ES operation significantly improves system performance in reducing the system congestion. This robust optimization-based ES operation can further increase system flexibility to facilitate more renewable energy and flexible demand without triggering the large-scale network investment