Influence of Remote Internal Tides on the Locally Generated Internal Tides upon the Continental Slope in the South China Sea

In this paper, the M2 internal tides (ITs) originating from the continental slope in the South China Sea are studied using the CROCO model. The simulation results show that there are two origins of ITs on the continental slope: at 118°–119.5° E along 22° N near the southern entrance of the Taiwan Strait and at 117°–118° E along 20° N near Dongsha Island. The local generation of ITs is greatly influenced by the ITs that radiate from the Luzon Strait (LS). The integrated conversion at the first generation site is increased by 31% to 0.42 GW compared to the case where the LS is excluded from the simulation region. Its maximum energy flux almost doubles to 2.5 kW/m, which is 10% of the westward component. The existence of the other IT beams from Dongsha Island is attributed to the ITs from the LS. The local generation on the continental slope changes when remotely generated ITs alter the amplitudes and phases of the bottom pressure perturbation. These results indicate that the ITs originating from the LS contribute to the spatial variation of ITs in the SCS by modulating the IT generation on the continental slope.

A statistics-based review of microgrid protection with a concentration on adaptive protection

Microgrid (MG) is a system of production and distribution of electrical energy that can operate both in grid-connected and islanded modes. This capability leads to significant variations in the fault current level. Moreover, dynamic changes corresponding to the line outage contingencies or outages of the distributed generations (DGs) that are implemented for local generation in the MGs lead to the changes in the fault current level. These changes in the fault current level make some miscoordination between the overcurrent relays (OCRs) in the conventional protection schemes. To overcome this drawback, there is a need for an adaptive protection scheme that can adapt to both operational and dynamic changes and takes effective protection decisions accordingly. This paper first presents a statistic-based review of MG and its protection including total publications, type of publications, the ten most researchers, and the ten most sources. Finally, comprehensive remarks of the 30 most cited papers related to adaptive protection of MG are presented. This paper will help the researchers of the MG protection to learn the most desirable techniques and the concentration of studies in adaptive protection of MGs for future works.

Towards Imposing Dayparted Restrictions on Tokenised Energy within Peer-to-Peer Markets

This piece proposes a novel mechanism for peer-to-peer electricity trading whereby energy tokens can only be redeemed in the same part of the day as when they were generated. The aim of this regulatory mechanism is to reduce token hoarding by consumers to better align the physical production and consumption of electricity, which in turn could decrease electrical system losses and minimise the chance of grid imbalances. To establish the effectiveness of this dayparting mechanism a market simulation is performed. This simulation is made up of 24 consumers' and five producers' profiles over a seven-day week. An optimisation is performed to most effectively allocate energy tokens from producers to consumers, aiming to minimise the total energy imported from the larger grid i.e. to make most effective use of local generation. Consumers are permitted to perform a measure of demand response by modulating their demand at certain points while keeping their total energy consumption constant. Allocated energy tokens can be consumed immediately, or during any subsequent daypart to the same type. A series of power flow analyses are performed using the market simulation out-turns to establish the electrical system effects. Consumers are found to move some demand to weekend days when demand is lower but generation is equally abundant. Electrical results reveal a decrease in system losses, as well as less fluctuation from the larger grid supply.

Towards Imposing Dayparted Restrictions on Tokenised Energy within Peer-to-Peer Markets

This piece proposes a novel mechanism for peer-to-peer electricity trading whereby energy tokens can only be redeemed in the same part of the day as when they were generated. The aim of this regulatory mechanism is to reduce token hoarding by consumers to better align the physical production and consumption of electricity, which in turn could decrease electrical system losses and minimise the chance of grid imbalances. To establish the effectiveness of this dayparting mechanism a market simulation is performed. This simulation is made up of 24 consumers' and five producers' profiles over a seven-day week. An optimisation is performed to most effectively allocate energy tokens from producers to consumers, aiming to minimise the total energy imported from the larger grid i.e. to make most effective use of local generation. Consumers are permitted to perform a measure of demand response by modulating their demand at certain points while keeping their total energy consumption constant. Allocated energy tokens can be consumed immediately, or during any subsequent daypart to the same type. A series of power flow analyses are performed using the market simulation out-turns to establish the electrical system effects. Consumers are found to move some demand to weekend days when demand is lower but generation is equally abundant. Electrical results reveal a decrease in system losses, as well as less fluctuation from the larger grid supply.

Jump Linear Quadratic Control for Microgrids with Commercial Loads

Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of μGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of μGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models μGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach.

Towards Embedding Network Usage Charges Within a Peer-to-Peer Electricity Marketplace

<div>This paper proposes a novel tariff regime for peerto-peer energy trading, with an aim to increase transmission</div><div>efficiency and grid stability by penalising long distance power transactions. In this scheme a portion of the transacted energy is withheld based on the electrical distance between buying and selling parties, calculated here according to the Klein Resistance Distance. This tariff regime is simulated using a dataset of producers and consumers over a 24-hour period. First, a notional marketplace equilibrium simulation is performed, in which</div><div>consumers can optimally activate demand response resources to exploit local availability of energy. Consumers are observed to move some demand away from peak times to make use of local generation availability. These simulated market out-turns are then used as inputs to a time series power flow analysis, in order to evaluate the network’s electrical performance. The regime is found to decrease grid losses and the magnitude of global voltage angle separation. However, the metric whereby taxes are calculated is found to be too skewed in the utility’s favour and may discourage adoption of the peer-to-peer system.</div><div>The method also attempts to encourage regulatory adoption</div><div>by existing grid operators and utilities. Some counter-intuitive allocations of tokenised energy occur, owing to specific consumers’ demand profiles and proximity to generators.</div><div><br></div>

Towards Embedding Network Usage Charges Within a Peer-to-Peer Electricity Marketplace

<div>This paper proposes a novel tariff regime for peerto-peer energy trading, with an aim to increase transmission</div><div>efficiency and grid stability by penalising long distance power transactions. In this scheme a portion of the transacted energy is withheld based on the electrical distance between buying and selling parties, calculated here according to the Klein Resistance Distance. This tariff regime is simulated using a dataset of producers and consumers over a 24-hour period. First, a notional marketplace equilibrium simulation is performed, in which</div><div>consumers can optimally activate demand response resources to exploit local availability of energy. Consumers are observed to move some demand away from peak times to make use of local generation availability. These simulated market out-turns are then used as inputs to a time series power flow analysis, in order to evaluate the network’s electrical performance. The regime is found to decrease grid losses and the magnitude of global voltage angle separation. However, the metric whereby taxes are calculated is found to be too skewed in the utility’s favour and may discourage adoption of the peer-to-peer system.</div><div>The method also attempts to encourage regulatory adoption</div><div>by existing grid operators and utilities. Some counter-intuitive allocations of tokenised energy occur, owing to specific consumers’ demand profiles and proximity to generators.</div><div><br></div>

Demand Response Optimization Model to Energy and Power Expenses Analysis and Contract Revision

This paper proposes a mathematical model based on mixed integer linear programming (MILP). This model aids the decision-making process in local generation use and demand response application to power demand contract adequacy by Brazilian consumers/prosumers. Electric energy billing in Brazil has some specificities which make it difficult to consider the choice of the tariff modality, the determination of the optimal contracted demand value, and demand response actions. In order to bridge this gap, the model considers local generation connected to the grid (distributed generation) and establishes an optimized solution indicating power energy contract aspects and the potential reduction in expenses for the next billing period (12 months). Different alternative sources already available or of interest to the consumer can be considered. The proposed mathematical model configures an optimization tool for the feasibility analysis of local generation use and, concomitantly, (i) checking the tariff modality, (ii) revising the demand contract, and (iii) suggesting demand response actions. The presented result shows a significant reduction in the energy and power expenses, which confirms the usefulness of this proposal. In the end, the optimized answers promote benefits for both, the consumer/prosumer and the electric utility.