Probabilistic adaptive model predictive power pinch analysis (PoPA) energy management approach to uncertainty

This paper proposes a probabilistic power pinch analysis (PoPA) approach based on Monte–Carlo simulation (MCS) for energy management of hybrid energy systems uncertainty. The systems power grand composite curve is formulated with the chance constraint method to consider load stochasticity. In a predictive control horizon, the power grand composite curve is shaped based on the pinch analysis approach. The robust energy management strategy effected in a control horizon is inferred from the likelihood of a bounded predicted power grand composite curve, violating the pinch. Furthermore, the response of the system using the energy management strategies (EMS) of the proposed method is evaluated against the day-ahead (DA) and adaptive power pinch strategy

Improving the efficiency of renewable energy assets by optimizing the matching of supply and demand using a smart battery scheduling algorithm

Given the fundamental role of renewable energy assets in achieving global temperature control targets, new energy management methods are required to efficiently match intermittent renewable generation and demand. Based on analysing various designed cases, this paper explores a number of heuristics for a smart battery scheduling algorithm that efficiently matches available power supply and demand. The core of improvement of the proposed smart battery scheduling algorithm is exploiting future knowledge, which can be realized by current state-of-the-art forecasting techniques, to effectively store and trade energy. The performance of the developed heuristic battery scheduling algorithm using forecast data of demands, generation, and energy prices is compared to a heuristic baseline algorithm, where decisions are made solely on the current state of the battery, demand, and generation. The battery scheduling algorithms are tested using real data from two large-scale smart energy trials in the UK, in addition to various types and levels of simulated uncertainty in forecasts. The results show that when using a battery to store generated energy, on average, the newly proposed algorithm outperforms the baseline algorithm, obtaining up to 20–60% more profit for the prosumer from their energy assets, in cases where the battery is optimally sized and high-quality forecasts are available. Crucially, the proposed algorithm generates greater profit than the baseline method even with large uncertainty on the forecast, showing the robustness of the proposed solution. On average, only 2–12% of profit is lost on generation and demand uncertainty compared to perfect forecasts. Furthermore, the performance of the proposed algorithm increases as the uncertainty decreases, showing great promise for the algorithm as the quality of forecasting keeps improving

Real-time control of distributed batteries with blockchain-enabled market export commitments

Recent years have seen a surge of interest in distributed residential batteries for households with renewable generation. Yet, assuring battery assets are profitable for their owners requires a complex optimisation of the battery asset and additional revenue sources, such as novel ways to access wholesale energy markets. In this paper, we propose a framework in which wholesale market bids are placed on forward energy markets by an aggregator of distributed residential batteries that are controlled in real time by a novel Home Energy Management System (HEMS) control algorithm to meet the market commitments, while maximising local self-consumption. The proposed framework consists of three stages. In the first stage, an optimal day-ahead or intra-day scheduling of the aggregated storage assets is computed centrally. For the second stage, a bidding strategy is developed for wholesale energy markets. Finally, in the third stage, a novel HEMS real-time control algorithm based on a smart contract allows coordination of residential batteries to meet the market commitments and maximise self-consumption of local production. Using a case study provided by a large UK-based energy demonstrator, we apply the framework to an aggregator with 70 residential batteries. Experimental analysis is done using real per minute data for demand and production. Results indicate that the proposed approach increases the aggregator’s revenues by 35% compared to a case without residential flexibility, and increases the self-consumption rate of the households by a factor of two. The robustness of the results to uncertainty, forecast errors and to communication latency is also demonstrated

Efficient methods for approximating the Shapley value for asset sharing in energy communities

With the emergence of energy communities, where a number of prosumers invest in shared renewable generation capacity and battery storage, the issue of fair allocation of benefits and costs has become increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we examine a number of methods for approximating the Shapley value in realistic community energy settings, and propose a new one. To compare the performances of these methods, we also design a novel method to compute the Shapley value exactly, for communities of up to several hundred agents by clustering consumers into a smaller number of demand profiles. We compare the methods in a large-scale case study of a community of up to 200 household consumers in the UK, and show that our method can achieve very close redistribution to the exact Shapley values but at a much lower (and practically feasible) computation cost

Efficient methods for approximating the Shapley value for asset sharing in energy communities

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings — however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of- the-art method that uses adaptive sampling (O’Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower

The economics of Theocracy

This paper models theocracy as a regime where the clergy in power retains knowledge of the cost of political production but which is potentially incompetent or corrupt. This is contrasted with a secular regime where government is contracted out to a secular ruler, and hence the church loses the possibility to observe costs and creates for itself a hidden-information agency problem. The church is free to choose between regimes – a make-or-buy choice – and we look for the range of environmental parameters that are most conducive to the superiority of theocracy and therefore to its occurrence and persistence, despite its disabilities. Numerical solution of the model indicates that the optimal environment for a theocracy is likely to be one in which the “bad” (high-cost) state is disastrously bad but the probability of its occurrence is not very high. A broad review of the historical evidence yields some suggestive support to the predictions of the model. Finally, the model is shown to be applicable to the make-or-buy-government choices of other groups, such as organized labor and the military

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Partial altitudinal migration of a Himalayan forest pheasant

BACKGROUND: Altitudinal migration systems are poorly understood. Recent advances in animal telemetry which enables tracking of migrants across their annual cycles will help illustrate unknown migration patterns and test existing hypotheses. Using telemetry, we show the existence of a complex partial altitudinal migration system in the Himalayas and discuss our findings to help better understand partial and altitudinal migration. METHODOLOGY/PRINCIPAL FINDINGS: We used GPS/accelerometer tags to monitor the migration of Satyr tragopan (Tragopan satyra) in the Bhutan Himalayas. We tagged 38 birds from 2009 - 2011 and found that tragopans are partially migratory. Fall migration lasted from the 3(rd) week of September till the 3(rd) week of November with migrants traveling distances ranging from 1.25 km to 13.5 km over 1 to 32 days. Snowfall did not influence the onset of migration. Return migration started by the 1(st) week of March and lasted until the 1(st) week of April. Individuals returned within 4 to 10 days and displayed site fidelity. One bird switched from being a migrant to a non-migrant. Tragopans displayed three main migration patterns: 1) crossing multiple mountains; 2) descending/ascending longitudinally; 3) moving higher up in winter and lower down in summer. More females migrated than males; but, within males, body size was not a factor for predicting migrants. CONCLUSIONS/SIGNIFICANCE: Our observations of migrants traversing over multiple mountain ridges and even of others climbing to higher elevations is novel. We support the need for existing hypotheses to consider how best to explain inter- as well as intra-sexual differences. Most importantly, having shown that the patterns of an altitudinal migration system are complex and not a simple up and down slope movement, we hope our findings will influence the way altitudinal migrations are perceived and thereby contribute to a better understanding of how species may respond to climate change