Grid Area Research Articles

Irrigation management zone strategies impact assessment on potential crop yield, water and energy savings

In irrigated agriculture, soil spatial variability is a primary factor for low irrigation efficiency. In regions such as the Brazilian Cerrado, where there is a continuous growth of irrigated agriculture and limited water supplies, it is important to seek alternatives to reach an efficient and sustainable irrigated agriculture. In this context, precision irrigation has great potential. Better strategies must be established, especially for center pivot irrigation conditions. Irrigation considering management zones is a promising option; however, criteria must be set to delimitate management zones and evaluate irrigation performance in such situations. This study aims to assess the impact of the use of management zones on irrigation performance. Therefore, two center pivots, PivoBHBV and PivoBHALPA, with distinct soil water physical traits, were evaluated. Two management zones strategies were considered to carry out the simulations. One of the strategies was based on regions in the format of square grids with areas of 25, 100 and 225 m2. In the other strategy, the unsupervised grouping algorithm Fuzzy C-Means was used to create management zones. The irrigation performed on those management zones was assessed by comparing their performance with the irrigation conducted in the grid areas. The irrigation management zones were delimited based on the interpolated maps of soil available water capacity (AWC), and the management was conducted individually for each zone. The simulations were made through a model developed with the Python language. The results indicate similarity between the irrigation with management zones and the grid irrigation, as it did not significantly increase water and energy demands or reduce soybean yield. Precision irrigation practiced management zones increased irrigation efficiency, reducing the effect of soil spatial variability. The total irrigation depth applied was, on average, 1.47 % lower than the irrigation by grids. Comparing the grid and the management zones irrigations, water and energy savings potential and the average yield increase potential were 1.85 %, 1.75 %, and 0.41 %.

Monitoring System for Overhead Power Transmission Lines in Smart Grid System Using Internet of Things

In the traditional systems, detection of the faults in the electric lines is more difficult. Detection of faults takes time more than repairing time. In most cases faults or problems in power transmission lines occur due to environmental disturbance. Advanced monitoring system of overhead transmission lines assists in saving human lives and helps in maintenance planning. Tension and loosening are examples of problems that result in the absence of high quality monitoring system. The objective of this paper is to do continuous monitoring of the atmospheric conditions and the sag status in the power transmission lines. The design consist of a group of sensors (distance sensor, wind speed sensor, pressure sensor and deviation sensor), which are used to monitor the atmospheric conditions, and send the records to main controller. The controllers transmits these measured values through Wi-Fi to the web page and display them in the channel graph. The recorded resulting data can be shown in a digital form at any time by selecting any point in the graph. The system is built up by using internet of things (IoT) monitoring technique and global positioning system (GPS) technology to provide the location and guarantee wide range of covering area of the smart grid. The experimental results include the date, time and value and is shown in graphical form to help the manager to analyze the system faults easily. The obtained results were reliable and correct. The results satisfied the goals of the paper and reveal that the use of IoT monitoring system of the transmission lines enhanced the quality of the service.

The effect of grid size on catch efficiency and by-catch in the demersal trawl fishery for Norway pout (Trisopterus esmarkii)

Norway pout (Trisopterus esmarkii) is caught with large trawls with small meshed codends, inevitably causing the fishery to have large by-catch issues. To reduce the amount of by-catch, a rigid sorting grid was made compulsory in 2010. However, there is still a severe by-catch issue, as well as the loss of target species because of the grid. A possible cause might be clogging of the grid, which could be solved by increasing the grid area. Therefore, this study compared the size selectivity of by-catch species and target species in a double-trawl configuration in which one trawl was equipped with a standard grid (6.30 m2), and the other trawl was equipped with a grid that had a 50% larger surface area (9.45 m2). The results demonstrated that the size selectivity and catch efficiency of the target species were unaffected; neither was there any significant difference between the two grids in terms of wanted by-catch species [blue whiting (Micromesistius poutassou), horse mackerel (Trachurus trachurus), and greater argentine (Argentina silus)]. However, the larger grid caught significantly more unwanted by-catch species [haddock (Melanogrammus aeglefinus), whiting (Merlangius merlangus), and hake (Merluccius merluccius)]. Approximately one-third of the accumulated catches comprised the target species, one-third of wanted by-catch species, and one-third of unwanted by-catch species, demonstrating the by-catch challenges in this fishery. Simultaneously, none of the by-catch limits were exceeded.

An Improved Transition Probability Matrix for Crime Distribution Prediction.

The occurrence of crime has always been the main problem affecting urban public security and social security environment. Therefore, the prevention and control of crime is the focus of public security work. The traditional police strategy has poor timeliness and cannot respond and adjust in real time with the occurrence of criminal activities, and its deterrence and control of criminal activities are limited. To address the problem of low accuracy of existing crime distribution prediction models, an improved transition probability matrix for crime distribution prediction is proposed in this paper. Based on a large number of trajectory data of criminals, this paper quantitatively describes the temporal and spatial characteristics of crowd movement in different areas of the city by using the temporal and spatial transfer probability. Then, combining Markov chain and Bayes' theorem, the probability model of spatio-temporal transfer of criminal groups in regions is constructed. Finally, the model predicts the number of crimes in urban grid areas.

Coordinated Charging and Discharging of Electric Vehicles: A New Class of Switching Attacks

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.

Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic

Accurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches.

A Feasibility Study of Placing a Heated Turbulence Grid in Front of an Air-Cooled Fuel Cell Stack in Freezing Conditions

The telecommunications industry is growing fast, and the air-cooled fuel cell is a promising alternative to the diesel-gensets as a backup power system. These systems are required to operate in freezing conditions where temperatures can be as low as -40°C, and they are usually located in remote areas where they can not be easily accessed. Berning proposed the placement of a turbulence grid in front of an air-cooled fuel cell stack to improve the heat transfer and thereby the performance, since the performance of the fuel cells was restricted due to the membrane overheating[1]. In an experimental study, Al Shakhshir et al. measured a performance increase of over 30% by placing a turbulence grid before an air-coiled fuel cell stack [2]. Then, Berning and Knudsen studied how the performance of the fuel cell would be affected while operating in different climate regions [3]. Additionally, previous studies allocated a turbulence inducing grid in front of the cathode inlet of the fuel cell where the main goal was to improve the heat transfer between the air and the components to achieve higher performances of the fuel cell while keeping the operating temperature in a good range [4] and how the design of this turbulence inducing grid would affect the cell [5]. Besides the use of a turbulence grid, this paper aims to analyze how the effect of the freezing ambient temperature can be reduced by pre-heating the incoming fuel cell air. A feasibility study is developed where different temperature differences and four different grid designs where the number of grid pores and its thickness are varied. The aims of this project were to identify feasible surface temperatures of the grid while having a reasonable consumption of fuel cell power to be provided to the grid and heated it up. A three-dimensional, steady state numerical analysis has been conducted on these grid dimensions where the computational domain considered was a single cathode channel where the gas diffusion layer would be the bottom surface, and the bipolar plate is to remain walls. Four different grid dimensions, namely grids with one and two millimeters of thickness and six 1 mm2 pores and grids with one and two millimeters of thickness and twenty-four 0.17 mm2 pores, have been considered. The outside temperature has been varied from -20 °C to 0 °C and heated up to 5 °C before air flow enters the cathode. The goal of this study is to better understand the temperature of the turbulence grid that is required to pre-heat the incoming air and thus conclude, which materials are suited. A parametric study was conducted, and with the last designed grid the best result was achieved. Preheating the air by 10°C, a surface temperature around 236°C was predicted. The fuel cell power usage would be approximately 30% of the total provided by fuel cell. It is possible to conclude the surface area of a single grid is not sufficient, leading to too high temperatures. A possibility could be having two staggered grids. Finally, an energy storage system could be used to provide this additional power required, avoiding such consumption of hydrogen and its slow dynamic response. [1] Berning T. A Numerical Investigation of Heat and Mass Transfer in Air-Cooled Proton Exchange Membrane Fuel Cells. InFluids Engineering Division Summer Meeting 2019 Jul 28 (Vol. 59032, p. V002T02A030). American Society of Mechanical Engineers.[2] Shakhshir SA, Gao X, Berning T. An experimental study of the effect of a turbulence grid on the stack performance of an air-cooled proton exchange membrane fuel cell. Journal of Electrochemical Energy Conversion and Storage. 2020 Feb 1;17(1):011006.[3] Berning T, Knudsen Kær S. A thermodynamic analysis of an air-cooled proton exchange membrane fuel cell operated in different climate regions. Energies. 2020 Jan;13(10):2611.[4] Lind A, Yin C, Berning T. A Computational Fluid Dynamics Analysis of Heat Transfer in an Air-Cooled Proton Exchange Membrane Fuel Cell with Transient Boundary Conditions. ECS Transactions. 2020 Sep 8;98(9):255.[5] Pløger LJ, Fallah R, Al Shakhshir S, Berning T, Gao X. Improving the Performance of an Air-Cooled Fuel Cell Stack by a Turbulence Inducing Grid. ECS Transactions. 2018 Jul 23;86(13):77. Figure 1

Precipitation Trends Analysis Using Gridded Dynamic Sampling Zones: Case Study Yangtze Delta Megalopolis

As a result of the fast growth of remote sensing and data assimilation technology, many global land use land cover (LULC) and climate reanalysis data sets have been used to advance our understanding of climate and environmental change. This paper investigates the precipitation variations of the Yangtze Delta Megalopolis by using precipitation reanalysis data under conditions of dynamic urban sprawl. Compared with current precipitation characteristic analyses, which are often based on a limited number of ground rainfall stations, the approach followed in this study comprises a grid-based statistical method using large sets of samples with a uniform distribution and a same representative grid area. This novel approach of dynamic sampling is applied in this study to overcome the temporal and spatial inconsistency of stationary sampling. This approach allows to examine the impact of urbanization on regional precipitation characteristics. The Yangtze Delta Megalopolis (YDM) region, one of the most developed regions in China, was selected as a case study to evaluate the impact of urbanization on subsequent precipitation features. The results reveal that the annual total precipitation (TP) and the maximum daily precipitation (MDP) in both urban and non-urban areas of the YDM region generally have increased during the past 30 years. Hence, the region has become increasingly humid. Extrema of annual MDP and TP show obvious spatial characteristics, in which most maxima are located in the southern part of YDM while minima are more concentrated in the northern part. This newly developed approach has potentials for application in studies where underlying surface features exhibit rapid alterations. The findings of this case study provide relevant information for planning and design of regional water resources management, flood risk management, and planning of the urban drainage system of the YDM region.

An innovative partition method for predicting shallow landslides by combining the slope stability analysis with a dynamic neural network model

The objective of this study is to explore the relevance between the rainfall pattern, slope stability of the soil layer, and the occurrence of shallow landslides. A seepage flow model for subsurface flow simulation with a slope stability analysis approach was established to examine the temporal and spatial variation of unstable grids. To define the threshold conditions to trigger shallow landslides for different regions in the watershed, the spatial distribution of the safety factor at the initial state was derived and adopted as the reference basis to classify all grids of the watershed into multiple zones. The first novelty of this study is to apply such a partition, depending on the watershed topography and soil characteristics, to train the landslide prediction model separately. The second novelty is to execute a dynamic recurrent neural network (RNN) model to determine the possible duration and the start time of shallow landslides for each zone by considering the rainfall condition as well as the cumulative area of unstable grids, which can be obtained by performing the seepage flow model. In this way, the physical significance of the RNN prediction model can be reinforced. The analysis results showed that the proposed methodology could effectively track the respective period of occurring shallow landslides for each zone, additionally, only some specific zones in the watershed were necessary to be investigated because they were prone to cause a large number of grids to become unstable during rainstorms.

Analysis of future electric mobility scenarios based on synthetic, georeferenced grid models

The efforts to reduce the emission of greenhouse gases in the transport sector lead to an increase of electric vehicles and charging points. To assess the impact of electric vehicle charging on distribution grids, detailed models are required that take spatial and socioeconomic differences of regions into account. This publication presents a method for the bottom-up modelling of medium- and low-voltage grids based on georeferenced supply tasks. Further, a method to model future mobility scenarios for electric vehicles in a spatial resolution and the integration of these scenarios into the modelled grids is presented. The plausibility of the modelled grids is analysed by comparison to structural data of real grids. Finally, the results of yearly simulations for multiple scenarios for an exemplary grid area are presented to demonstrate the impact of the increase in electric vehicles and charging points on the electrical distribution grids.

Planning of an MVDC interconnection considering an increasing penetration of E-Mobility

The challenges concerning the electrical grid will continue to change. In addition to the expansion of decentralized energy generators such as photovoltaics and wind turbines, the load behavior of consumers will be recharacterized. Against this background, actively controllable operating resources are becoming more important for distribution system operators for optimizing grid operations. Considering an increasing penetration of charging stations for electric vehicles, the grid can be brought to its technical operating limits. This paper presents a model-based approach for determining the influence of electric vehicles on the operational points of a medium voltage direct current (MVDC) connection. Therefore, the planning process of an MVDC in a real grid in Northern Germany is introduced. The MVDC connection is dimensioned and deployed to relieve an overloaded line in the central grid area by actively controlling power flow and providing inductive reactive power. By this, the penetration rate of charging stations can be increased by 10%. Finally, the research shows that the grid operation can be optimized with the help of the MVDC connection, allowing more distributed energy resources to be integrated into the grid.

Application of Visual Communication Combined with Electrochemistry in Ceramic Carving Product Design

In order to improve the design of ceramic carving products, the author proposes a visual communication, combined with electrochemistry, laser engraving technology, using superpixel imaging technology, carry out laser engraving ceramic reticulation imaging processing, build a binary laser engraving image model, using image edge feature matching method, carry out the edge contour feature detection of the laser engraved ceramic reticulated image, and extract the local information feature quantity of the laser engraved ceramic mesh image. The fuzzy adaptive segmentation method is used for the extracted features and to perform grid area segmentation. The experimental results show that the greater the laser output power, the deeper the engraving depth. As the laser output power increases, the depth of the hole formed by a single pulse laser is enlarged, the etched area formed by the dense hole group also deepens. However, the experiment found that after the laser power is increased to a certain value (17 A), the temperature of the light spot rises sharply, reach the melting point of ceramics, and produce slag that is not easy to remove on the engraved surface, decrease the effective engraving depth. It proves that visual communication, combined with electrochemistry, can effectively realize the design of ceramic carving products.

Accurate modulation of photoprinting under stiffness imaging feedback for engineering ECMs with high-fidelity mechanical properties

Engineered extracellular matrices (ECMs) that replicate complex in-vivo features have shown great potential in tissue engineering. Biocompatible hydrogel microstructures have been widely used to replace these native ECMs for physiologically relevant research. However, accurate reproduction of the 3D hierarchical and nonuniform mechanical stiffness inside one integrated microstructure to mimic the complex mechanical properties of native ECMs presents a major challenge. Here, by using digital holographic microscopy (DHM)-based stiffness imaging feedback, we propose a novel closed-loop control algorithm to achieve high-accuracy control of mechanical properties for hydrogel microstructures that recapitulate the physiological properties of native ECMs with high fidelity. During photoprinting, the photocuring area of the hydrogel is divided into microscale grid areas to locally control the photocuring process. With the assistance of a motorized microfluidic channel, the curing thickness is controlled with layer-by-layer stacking. The DHM-based stiffness imaging feedback allows accurate adjustment of the photocuring degree in every grid area to change the crosslinking network density of the hydrogel, thus enabling large-span and high-resolution modulation of mechanical properties. Finally, the gelatin methacrylate was used as a typical biomaterial to construct the high-fidelity biomimetic ECMs. The Young’s modulus could be flexibly modulated in the 10 kPa to 50 kPa range. Additionally, the modulus gradient was accurately controlled to within 2.9 kPa. By engineering ECM with locally different mechanical properties, cell spreading along the stiff areas was observed successfully. We believe that this method can regenerate complex biomimetic ECMs that closely recapitulate in-vivo mechanical properties for further applications in tissue engineering and biomedical research.

Method for Tsunami Wave Kinematics Modeling in a Gridded Area

The new method for the tsunami travel times calculation to the nodes of rectangular grid was developed and tested. The algorithm is based on constructing of the wave front segment inside the grid cell using tsunami arrival times to the cell corners. Testing the method proposed shows the better quality of the wave-front line approximation as compared to the existing method developed by the author earlier.

Financial Analysis Of A Hybrid Tidal Stream Energy System For Sustainable Island Electrification In The Philippines

Tidal Stream Energy (TSE) is emerging as an innovative solution to provide clean and reliable energy. TSE also has the benefit to conserve land, since the turbines are located in an offshore environment with strong tidal flows. This energy source has the opportunity to make a major difference in off grid areas, such as those found in the Philippines. As an archipelago of over 7000 islands, there are many “off grid” locations which are underserved or completely unserved with electricity. The Philippine government has instituted a missionary electrification program designed to help the many off grid locations throughout the country. However, providing electricity for the citizens of these locations is difficult due to the high cost of produced energy in remote areas. To assist in this, the government has created a Universal Charge for Missionary Electrification (UCME), which is a subsidy from developed areas distributed to missionary areas to improve the affordability of generated power. However, the subsidy algorithm is largely based upon the OPEX of diesel plants.

Optimization of Numerical Methods for Transforming UTM Plane Coordinates to Lambert Plane Coordinates

The rapid transformation from UTM (Universal Transverse Mecator) projection to Lambert projection helps to realize timely merging, inversion, and analysis of high-frequency partitioned remote sensing images. In this study, the transformation error and the efficiency of the linear rule approximation method, the improved linear rule approximation method, the hyperbolic transformation method, and the conformal transformation method were compared in transforming the coordinates of sample points on WGS84 (The World Geodetic System 1984)-UTM zonal projections to WGS84-Lambert projection coordinates. The effect of the grid aspect ratio on the coordinate transformation error of the conformal transformation method was examined. In addition, the conformal transformation method-based error spatial pattern of the sample points was analyzed. The results show that the conformal transformation method can better balance error and efficiency than other numerical methods. The error of the conformal transformation method is less affected by grid size. The maximum x-error is less than 0.36 m and the maximum y-error is less than 1.22 m when the grid size reaches 300 km × 300 km. The x- and y-error values decrease when square grids are used; namely, setting the grid aspect ratio close to 1 helps to weaken the effect of increasing grid area on the error. The dispersion of the error distribution and the maximum error of sample points both decrease relative to their minimum distance to the grid edge and stabilize at a minimum distance equal to 70 km. This study can support the rapid integration of massive remote sensing data over large areas.

Ecosystem health assessment in debris flow-prone areas: A case study of Bailong River Basin in China

Ecosystem health assessment (EHA) is critical for the effective management of ecosystem services and evaluation of global environmental changes. Here, we explored the spatiotemporal patterns of EH in the Bailong River Basin in a debris flow-prone area from 2000 to 2019 using the VORS model. Net primary productivity, landscape index, land use land cover, water yield, and soil retention data were used to assess the vigor, organization, resilience, and services of the ecosystem. Moreover, using the spatial correlation method and hierarchical cluster analysis, the spatial clustering of and regional differences in EH were investigated in grid and catchment area. In the grid scale, the EH is optimal in the southwest but poor in the northeast. In most areas of the basin, the ecological health index (EHI) showed an increasing trend, and this pattern is expected to continue in the future from 2000 to 2019. The EHI showed significant global spatial correlation and local spatial positive correlation; however, the degree of clustering showed a gradual decline from 2000 to 2019. Based on the results of hierarchical clustering, the catchment area in the basin was divided into four clusters to elucidate regional differences in EH. Areas with high EHI were mainly located in the southern part of the basin, whereas those with low EHI were located in the east of the Wudu District and north of the Tanchang County. Overall, EHA is an important tool to obtain information on the state of the ecosystem, and its findings are fundamental for protecting and improving EH. Therefore, EHA should be incorporated in the decision-making framework for realising effective and appropriate ecosystem management.

Advanced metering infrastructure smart metering based on cloud architecture for low voltage distribution networks in application of smart grid monitoring

In most circumstances, accessing measurements from entire smart metres set in less-voltage grid area for distribution monitoring is not practicable because of less bandwidth as well as excessive delays in accessing Smart Meter monitoring. Estimating the condition of a distribution system can be done using voltage, active and reactive power measurements from smart metres subset. On one hand, increasing number of selected smart metres improves distribution state assessment accuracy, it degrades monitoring data time. It improves the electrical system's dependability and efficiency while also providing security to the system. Smart grid infrastructure is a modified form of smart grid. Smart grid infrastructure is a modified form of the smart grid. It has high-capacity converters, sensing and metering technologies, and automated control to improve efficiency and dependability. The goal of this article is to use smart metering technology to improve the observer of low-voltage distribution grids. The use of a distribution network-based state estimation technique employing smart metre readings for near-real-time monitoring of low-voltage distribution grids is the focus of this research. This paper propose smart metering based on cloud architecture for low voltage distribution networks with distribution network based state estimation algorithm in application of monitoring. The article will discuss the critical role of cloud solution in achieving interoperability, scalability and flexibility as well as enabling integration of various services for distribution system automation. As an example of coordinated operation of diverse distribution grid services by cloud, distributed state estimation technique as well as autonomous network reconfiguration will be demonstrated.

An Information Fusion Model between GeoSOT Grid and Global Hexagonal Equal Area Grid

In order to cope with the rapid growth of spatiotemporal big data, data organization models based on discrete global grid systems have developed rapidly in recent years. Due to the differences in model construction methods, grid level subdivision and coding rules, it is difficult for discrete global grid systems to integrate, share and exchange data between different models. Aiming at the problem of information fusion between a GeoSOT grid and global hexagonal equal area grid system, this paper proposes the GeoSOT equivalent aggregation model (the GEA model). We establish a spatial correlation index method between GeoSOT grids and global hexagonal equal area grids, and based on the spatial correlation index, we propose an interoperable transformation method for grid attributes information. We select the POI (points of interest) data of Beijing bus and subway stations and carry out the transformation experiment of hexagonal grid to GeoSOT grid information so as to verify the effectiveness of the GEA model. The experimental results show that when the 17th-level GeoSOT grid is selected as the particle grid to fit the hexagonal grid, the accuracy and efficiency can be well balanced. The fitting accuracy is 95.51%, and the time consumption is 30.9 ms. We establish the associated index of the GeoSOT grid and the hexagonal grid and finally realized the exchange of information.

Multiperiod Dynamic Programming Algorithm for Optimizing a Nature Reserve

Zoning adjustments are a key method of improving the conservation efficiency of a nature reserve. Existing studies typically consider the one-period programming method and ignore dynamic ecological changes during the programming of a nature reserve. In this study, a scientific method for nature reserve (NR) programming, namely the multiperiod dynamic programming (MDP) algorithm, is proposed. The MDP algorithm designs an NR over three periods and does so by using ecological suitability values for each grid area. Ecological suitability values for each period were determined based on existing data on rare aquatic animals with Maxent software and cellular automata (CA). CA were used to determine the actual protection effect and to adjust each period’s ecological suitability values through comparisons with the sites’ surroundings. The maximization of ecological suitability values was used as an objective function; these values were assumed to indicate protection benefits. The objective function of the MDP also includes grid perimeters and numerical minimization for spatial compactness. Moreover, we designed three MDP constraints for the dynamic programming, including base constraints, distinguishing constraints, and multiperiod constraints. In the base and distinguishing constraints, we require a grid square to be a core, buffer, or unselected square, and we require the core and buffer grids to be spatially connected. For the multiperiod constraints, we used virtual points to ensure spatial continuity in different periods while attaining high ecological suitability. Our main contributions are as follows: (1) the novel MDP algorithm combining ecological attributes and multiperiod dynamic planning to optimize NR planning; (2) the use of virtual points to avoid selecting invalid grids and to ensure spatial continuity with significant protection benefits; and (3) the definition of ecological suitability values and use of CA to simulate dynamic changes over the three periods. The results reveal that the MDP algorithm results in a reserve with greater protection benefits than current reserves with superior spatial distribution due to multiperiod programming. The proposed MDP algorithm is a novel method for the scientific optimization and adjustment of nature reserves.