Cournot oligopoly game-based local energy trading considering renewable energy uncertainty costs

Facilitated by advanced information and communication technologies (ICTs), local energy trading develops rapidly, playing an important role in the energy supply chain. Thus, it is essential to develop local trading models and strategies that can benefit participants, not only stimulating local balancing but also promoting renewable penetration. This paper proposes a new local energy trading decision-making model for suppliers by using the Cournot Oligopoly game, considering the uncertainty costs of renewable energy. Four types of representative energy providers are modelled, traditional thermal generation, wind power, photovoltaic (PV) power, and electricity storage. The revenue of these technologies is extensively formulated according to their operation cost, investment cost, and income from selling energy. The uncertainty cost of renewable generation is integrated into the trading, modelled as a penalty for potential energy shortage that is derived from output probability distribution function (PDF). This trading model is formulated as a non-cooperative Cournot oligopoly game to enable energy suppliers to maximize their profits through local trading considering price. The response of the customer to energy price variations, i.e. demand elasticity, is also included in the model. A unique Nash equilibrium (NE) and optimum strategies are derived by the proposed Optimal-Generation-Plan (OGP) Algorithm. As demonstrated in a typical local market, the proposed approach can effectively model and resolve multiple suppliers’ competition in local energy trading. It can work as a vehicle to facilitate the trading between various generation technologies and customers, realising local balancing and benefiting all market participants with enhanced revenue and reduced energy bills.</p

LMP-based Pricing for Energy Storage in Local Market to Facilitate PV Penetration

Increasing Photovoltaic (PV) penetration and low-carbon demand can potentially lead to two different flow peaks, generation, and load, within distribution networks. This will not only constrain PV penetration but also pose serious threats to network reliability. This paper uses energy storage (ES) to reduce system congestion cost caused by the two peaks by sending cost-reflective economic signals to affect ES operation in responding to network conditions. First, a new charging and discharging (C/D) strategy based on binary search method is designed for ES, which responds to system congestion cost over time. Then, a novel pricing method, based on locational marginal pricing (LMP), is designed for ES. The pricing model is derived by evaluating ES impact on the network power flows and congestions from the loss and congestion components in LMP. The impact is then converted into an hourly economic signal to reflect ES operation. The proposed ES C/D strategy and pricing methods are validated on a real local grid supply point area. Results show that the proposed LMP-based pricing is efficient to capture the feature of ES and provide signals for affecting its operation. This work can further increase network flexibility and the capability of networks to accommodate increasing PV penetration.</p

Network pricing for customer-operated energy storage in distribution networks

Network pricing is essential for electricity system operators to recover investment and operation costs from network users. Current pricing schemes are only for generation and demand that purely withdraws or injects power from/into the system. However, they cannot properly price energy storage (ES), which has the dual characteristics of injecting and withdrawing power. This paper develops a novel pricing scheme for ESs in distribution systems operated by customers to reflect their impact on network planning and operation. A novel charging and discharging methodology is designed for ESs to respond to time of use tariffs for maximising electricity cost savings. The long-term incremental cost for ES is designed based on future reinforcement horizon and short-term operation cost is quantified by system congestion. Then, a novel pricing scheme for ES is designed by integrating the two costs. The pricing signals can guide ES operation to benefit both distribution network operators and ES owners. The new methodology is demonstrated on a small system with an ES of different features and then on a practical Grid Supply Point (GSP) area.</p

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

Melatonin protects against ovarian damage by inhibiting autophagy in granulosa cells in rats

Objectives: This study sought to further verify the protective mechanism of Melatonin (MT) against ovarian damage through animal model experiments and to lay a theoretical and experimental foundation for exploring new approaches for ovarian damage treatment. Method: The wet weight and ovarian index of rat ovaries were weighted, and the morphology of ovarian tissues and the number of follicles in the pathological sections of collected ovarian tissues were recorded. And the serum sex hormone levels, the key proteins of the autophagy pathway (PI3K, AKT, mTOR, LC3II, LC3I, and Agt5) in rat ovarian tissues, as well as the viability and mortality of ovarian granulosa cells in each group were measured by ELISA, western blotting, CCK8 kit and LDH kit, respectively. Results: The results showed that MT increased ovarian weight and improved the ovarian index in ovarian damage rats. Also, MT could improve autophagy-induced ovarian tissue injury, increase the number of primordial follicles, primary follicles, and sinus follicles, and decrease the number of atretic follicles. Furthermore, MT upregulated serum AMH, INH-B, and E2 levels downregulated serum FSH and LH levels in ovarian damage rats and activated the PI3K/AKT/mTOR signaling pathway. Besides, MT inhibited autophagic apoptosis of ovarian granulosa cells and repressed the expression of key proteins in the autophagic pathway and reduced the expression levels of Agt5 and LC3II/I. Conclusions: MT inhibits granulosa cell autophagy by activating the PI3K/Akt/mTOR signaling pathway, thereby exerting a protective effect against ovarian damage

Waiting Cost based Long-Run Network Investment Decision-making under Uncertainty

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and trigger a high asset risk. System operators have the incentive to postpone reinforcement, and &amp;#x2018;wait and see&amp;#x2019; whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties. The waiting cost is quantified by the options&amp;#x0027; cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty.</p

Waiting Cost based Long-Run Network Investment Decision-making under Uncertainty

Traditional system investment decision is costly and hard to reverse. This is aggravated by uncertainties from flexible load and renewables (FLR), which impact the accuracy of network investment decisions and trigger a high asset risk. System operators have the incentive to postpone reinforcement, and &amp;#x2018;wait and see&amp;#x2019; whether the request of investment can be reduced or delayed with new information. This paper proposes a novel method to evaluate network investment horizon deferral based on the trade-off between waiting profit and waiting cost under FLR uncertainties. Although deferring investment leads to waiting cost, it is worthy to wait if the cost is smaller than the waiting profits. To capture the impact of FLR uncertainties on system investment, nodal uncertainties are converted into branch flow uncertainties. The waiting cost is quantified by the options&amp;#x0027; cost based on real options method and waiting profit is from asset present value reduction due to the deferral. Thus, by paying waiting cost, current investment cost can be reserved until uncertainties are reduced to an acceptable level. The waiting time is evaluated by Sharp ratio and expected return, determined by the waiting cost and uncertainty level. The results show that paying waiting cost is an economical way to reduce the impact of uncertainty.</p

Optimal Borehole Energy Storage Charging Strategy in a Low Carbon Space Heat System

Domestic heating is the major demand of energy systems, which can bring significant uncertainties to system operation and shrink the security margin. From this aspect, the borehole system, as an interseasonal heating storage, can effectively utilize renewable energy to provide heating to ease the adverse impact from domestic heating. This paper proposes an optimal charging strategy for borehole thermal storage by harvesting energy from photovoltaic (PV) generation in a low-carbon space heating system. The system optimizes the heat injection generated by air source heat pump in the charging seasons to charge the borehole, which provides high inlet temperature for ground source heat pump to meet space heating demand in discharging seasons. The borehole is modeled by partial differential equations, solved by the finite-element method at both 2D and 3D for volume simulation. The pattern search optimization is used to resolve the model. The case study illustrates that with the optimal charging strategies, less heat flux injection can help the borehole to reach a higher temperature so that the heating system is more efficient compared with boilers. This paper can benefit communities with seasonable borehole storage to provide clean but low-cost heating and also maximize PV penetration.</p