Death spiral, transmission charges, and prosumers in the electricity market

Although electric power networks and district heating networks are physically coupled, they are not operated in a coordinated manner. With increasing penetration of renewable energy sources, a coordinated market-based operation of the two networks can yield significant advantages, as reduced need for grid reinforcements, by optimizing the power flows in the coupled systems. Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy. However, this approach has not been fully investigated in the context of market-based operation of coupled electric power and district heating networks. Therefore, this paper proposes a transactive control approach to coordinate flexible producers and consumers while taking into account the operational aspects of both networks, for the benefit of all participants and considering their privacy. A nonlinear model predictive control approach is applied in this work to maximize the social welfare of both networks, taking into account system operational limits, while reducing losses and considering system dynamics and forecasted power supply and demand of inflexible producers and consumers. A subtle approximation of the operational optimization problem is used to enable the practical application of the proposed approach in real time. The presented technique is implemented, tested, and demonstrated in a realistic test system, illustrating its benefits.

In engineering practice, extensive uncertainties exist in photovoltaic (PV) cell material parameters owing to manufacturing process errors and in PV cell working environment parameters owing to environmental uncertainties. Ignoring the uncertainty of these parameters will greatly reduce the reliability of photovoltaic cells. In this study, a PV cell model was used to conduct an uncertainty analysis based on functional failure. Functional failure is defined as output power fluctuation beyond the specified range, and functional safety region is defined as the allowable fluctuation range of output power during operation. A global sensitivity analysis method based on the Monte Carlo method was employed to study the influence of parameter fluctuation on the functional failure probability of a PV cell. An uncertainty analysis method based on Latin hypercube sampling was applied to study the influence of parameters on the probability of PV cell operation in the functional safety domain. The results indicate that the uncertainty of surface temperature had the most significant influence on the failure probability, followed by the ideality factor, radiation intensity, and current temperature coefficient, the series and parallel resistances had the smallest impact. Optimal values of 510.200 W/m², 284.600 K, and 1.446 were obtained for radiation intensity, surface temperature, and ideality factor, respectively, which ensured that system functional failure probability remained in the safety zone with values of 93.763 %, 99.211 %, and 98.856 %, respectively. Additionally, the lower series resistance and the higher parallel resistance, the more likely the system to operate in the functional safety domain. The results of study expand the evaluation standard for PV cell operation and ensure the desired system output power.

The market share of electric vehicles, powered by lithium-ion batteries (LIB), has been expanding worldwide with the global momentum towards green technology and improving the driving range on one full-charge. Studies are, however, still required on the fire safety of the latest but unmatured technology due to a distinctive phenomenon called thermal runaway. In this study, a series of full-scale fire experiments were conducted, focusing on the understanding of thermal behaviours of battery electric vehicle (BEV) fires. To provide up-to-date information on BEV fires, the latest BEV model with a high electric-energy capacity (64 kWh) was selected. For comparative analysis purposes, a LIB pack and a BEV body were tested individually after being physically disassembled. An internal combustion engine vehicle and a hydrogen fuel cell electric vehicle were also tested. During testing, the combustion of the BEV fires continued for approximately 70 min, resulting in critical measures of burning being determined; peak heat release rate (pHRR), total heat released (THR), fire growth parameter, and the average effective heat of combustion were measured to be 6.51–7.25 MW, 8.45–9.03 GJ, 0.0085–0.020, and 29.8–30.5 MJ/kg, respectively. It was also observed that the pHRR and THR were governed by the combustion characteristics of typical combustible materials in the passenger cabin, rather than by that of particular contents in the LIB pack with thermal runaway. Instead, a jet fire intensively discharging from the LIB pack led to a rapid flame spreading to adjacent combustible components of the BEV, thereby accelerating the fire growth. The findings could contribute to the activities of the first responders to BEV fire accidents, fire safety engineers, and structural member designers. This study also makes public the measured thermal quantities for further studies on the fire safety of existing or designing car-parking related structures.

In order to open the black box of carbon nanotube (CNT) growth, a thermotropic flash assembly (TFA) process in liquid phase at room temperature was innovatively proposed. The thermotropic assembly of CNTs via universal CVD method and an innovative TFA process were described in detail. The equivalent space of CNT growth on the surface of carbon fiber (CF) and different energy models were constructed for the TFA process. The growth equation of CNTs in the TFA process was derived for the first time, and the energy required to grow CNTs per unit mass was calculated to be 0.0214 kWh/g based on the growth conditions, which provided a basis for the growth of CNTs in different types of conductive media and the precise control of interface modification. Interestingly, different nanostructures (such as CNTs, nanolamellae, and nanoflower clusters) were found to grow on the CF surface in the TFA process. Moreover, the evolution process and the growth mechanism of CNTs in the TFA process were analyzed in detail. The analysis results were necessary to calculate the energy required for CNT growth. The power spectra of the continuous TFA and CVD processes were obtained online, and the input energy characteristics of the two processes were compared and calculated. It was concluded that the TFA process had superior homogeneity and stability in this respect, and it was an energy-conserving and efficient method of CNT growth. Therefore, the TFA process provided a new model for CNT growth, and the opened black box provided the possibility to deeply explore the mechanism of in situ thermotropic flash assembly of CNTs.

The increasingly complex energy systems are turning the attention towards model-free control approaches such as reinforcement learning (RL). This work proposes novel RL-based energy management approaches for scheduling the operation of controllable devices within an electric network. The proposed approaches provide a tool for efficiently solving multi-dimensional, multi-objective and partially observable power system problems. The novelty in this work is threefold: We implement a hierarchical RL-based control strategy to solve a typical energy scheduling problem. Second, multi-agent reinforcement learning (MARL) is put forward to efficiently coordinate different units with no communication burden. Third, a control strategy that merges hierarchical RL and MARL theory is proposed for a robust control framework that can handle complex power system problems. A comparative performance evaluation of various RL-based and model-based control approaches is also presented. Experimental results of three typical energy dispatch scenarios show the effectiveness of the proposed control framework.

Electric demand flexibility in buildings is highly dependent on occupant behavior. Evaluating and incentivizing these behaviors can provide grid-responsive support and encourage demand response (DR) participation. To achieve these goals, we developed an infrastructure for connecting Internet of Things (IoT) sensors to a distributed ledger (blockchain network) for long-term monitoring of energy and environmental performance. This study presents a novel Blockchain + IoT paradigm for the building science research community, applied in a real-world application. This Blockchain + IoT Network (BIN) uses Raspberry Pi minicomputers as platforms for connecting sensors to a blockchain network, to provide and analyze real-time indoor environmental quality (IEQ), energy, and carbon intensity data. As part of the study, we propose various metrics to evaluate the environmental footprints of building users. Novel algorithms for normalizing energy usage and carbon intensity, with consideration of a variety of related environmental factors, are executed as smart contracts on the blockchain network. All measurements and the smart contract transactions are reported and visualized on live dashboards. The use of smart contract allocates tokens based on the reward algorithms to incentivize individuals’ energy conservation, and similarly to DR pricing, can help influence occupant consumption patterns towards carbon reduction goals. We further test the smart contract’s algorithm in relation to real sensor data we have collected in two case studies: single-unit households and carbon intensity in the energy market. The combination of proposed metrics translates measured sensor data into token awards, demonstrates upper and lower limits dictated by the grid generation mix profile, and indicates that there is the potential for load shifting to minimize carbon emissions without considering the scale of consumption.